Climate Change AI Workshop Papers

On this page, we show accepted works from all our workshops on "Tackling Climate Change with Machine Learning."

Venue Title Subject Areas
ICLR 2024 Mapping Land Naturalness from Sentinel-2 using Deep Contextual and Geographical Priors (Papers Track)
Abstract and authors: (click to expand)

Abstract: In recent decades, the causes and consequences of climate change have accelerated, affecting our planet on an unprecedented scale. This change is closely tied to the ways in which humans alter their surroundings. As our actions continue to impact natural areas, using satellite images to see and measure these effects has become crucial for understanding and fighting climate change. Aiming to map land naturalness on the continuum of modern human pressure, we develop a multi-modal supervised deep learning framework that addresses the unique challenges of satellite data and the task at hand. We incorporate contextual and geographical priors. These priors are represented by corresponding coordinate information and broader contextual information including and surrounding the immediate patch to be predicted. Our framework improves the model's predictive performance to map land naturalness from a given Sentinel-2 data, a multi-spectral optical satellite imagery. Recognizing that our protective measures are as effective as our grasp of the ecosystem, quantifying naturalness serves as a crucial step towards enhancing our environmental stewardship.

Authors: Burak Ekim (University of the Bundeswehr); Michael Schmitt (University of the Bundeswehr Munich)

Ecosystems & Biodiversity Forests
ICLR 2024 Structured spectral reconstruction for scalable soil organic carbon inference (Papers Track)
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Abstract: Measuring soil organic carbon (SOC) inexpensively and accurately is crucial for soil health monitoring and agricultural decarbonization. Hyperspectral imaging is commonly evaluated as an inexpensive alternative to dry combustion for SOC measurement, but existing end-to-end approaches trained to predict SOC content from spectral data frequently fail to generalize when applied outside of their ground-truth geographic sampling distributions. Using stratified data from the USDA Rapid Carbon Assessment (RaCA), we demonstrate a method to improve model generalization out-of-distribution by training SOC regression alongside models that reconstruct input spectra. Because hyperspectra can be collected from remote platforms such as drones and satellites, this approach raises the possibility of using large hyperspectral Earth observation datasets to transfer SOC inference models to remote geographies where geographically-dense ground-truth data collection may be expensive or impossible. By replacing the decoder with a simple physics-informed model, we also learn an interpretable spectral signature of SOC, confirming its dark hue and expected reflectance troughs. Finally, we show that catastrophic generalization failures can be better addressed with these architectures by fine-tuning on large quantities of hyperspectral data.

Authors: Evan A Coleman (MIT); Sujay Nair (Georgia Institute of Technology); Xinyi Zeng (Coho Climate Advisors); Elsa Olivetti (MIT Department of Materials Science & Engineering)

Earth Observation & Monitoring Computer Vision & Remote Sensing
ICLR 2024 ClimateQ&A : bridging the gap between climate scientists and the general public (Papers Track)
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Abstract: This research paper investigates public views on climate change and biodiversity loss by analyzing questions asked to the ClimateQ&A platform. ClimateQ&A is a conversational agent that uses LLMs to respond to queries based on over 14,000 pages of scientific literature from the IPCC and IPBES reports. Launched online in March 2023, the tool has gathered over 30,000 questions, mainly from a French audience. Its chatbot interface allows for the free formulation of questions related to nature*. While its main goal is to make nature science more accessible, it also allows for the collection and analysis of questions and their themes. Unlike traditional surveys involving closed questions, this novel method offers a fresh perspective on individual interrogations about nature. Running NLP clustering algorithms on a sample of 3,425 questions, we find that a significant 25.8% inquire about how climate change and biodiversity loss will affect them personally (e.g., where they live or vacation, their consumption habits) and the specific impacts of their actions on nature (e.g., transportation or food choices). This suggests that traditional methods of surveying may not identify all existing knowledge gaps, and that relying solely on IPCC and IPBES reports may not address all individual inquiries about climate and biodiversity, potentially affecting public understanding and action on these issues. *Note: we use “nature” as an umbrella term for “climate change” and “biodiversity loss”.

Authors: Natalia de la Calzada (Ekimetrics); Theo Alves Da Costa (Ekimetrics); Annabelle Blangero (Ekimetrics); Nicolas CHESNEAU (EKIMETRICS)

Natural Language Processing Behavioral and Social Science
ICLR 2024 Improving Streamflow Predictions with Vision Transformers (Papers Track)
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Abstract: Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global Long Short-Term Memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-ViT-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a vision transformer. Applied to 531 basins across the United States (US), our method significantly outperforms existing models, showing an 11% increase in prediction accuracy. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for managing water resources under climate change.

Authors: Kshitij Tayal (Oak Ridge National Lab); Arvind Renganathan (University of Minnesota); Dan Lu (Oak Ridge National Laboratory)

Oceans & Marine Systems Time-series Analysis
ICLR 2024 Grapevine Disease Prediction Using Climate Variables from Multi-Sensor Remote Sensing Imagery via a Transformer Model (Papers Track)
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Abstract: Early detection and management of grapevine diseases are important in pursuing sustainable viticulture. This paper introduces a novel framework leveraging the TabPFN model to forecast blockwise grapevine diseases using climate variables from multi-sensor remote sensing imagery. By integrating advanced machine learning techniques with detailed environmental data, our approach significantly enhances the accuracy and efficiency of disease prediction in vineyards. The TabPFN model's experimental evaluations showcase comparable performance to traditional gradient-boosted decision trees, such as XGBoost, CatBoost, and LightGBM. The model's capability to process complex data and provide per-pixel disease-affecting probabilities enables precise, targeted interventions, contributing to more sustainable disease management practices. Our findings underscore the transformative potential of combining Transformer models with remote sensing data in precision agriculture, offering a scalable solution for improving crop health and productivity while reducing environmental impact.

Authors: Weiying Zhao (Deep Planet); Natalia Efremova (Queen Mary University London)

Agriculture & Food Earth Observation & Monitoring Causal & Bayesian Methods
ICLR 2024 Towards Ecological Network Analysis with Gromov-Wasserstein Distances (Papers Track)
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Abstract: Climate change is driving the widespread redistribution of species with cascading effects on predators and their prey. Formally comparing ecological interaction networks is a critical step towards understanding the impact of climate change on ecosystem functioning, yet current methods for ecological network analysis are unable to do so. We propose using the GromovWasserstein (GW) metric for quantifying dissimilarity between ecological networks. We demonstrates the corresponding optimal transport plans of this distance can be interpreted as species functional alignment between food webs. Our results show that GW transport plans align species from different mammal communities consistent with ecological understanding. Furthermore, we illustrate extensions of the GW distance to notions of averages and factorization over ecological networks. Ultimately, we propose the foundation for a novel interpretable topological data analysis framework to inform future ecological research and conservation management.

Authors: Kai M Hung (Rice University); Ann Finneran (Rice University); Alex Zalles (Rice University); Lydia Beaudrot (Rice University); Cesar Uribe (Rice University)

Ecosystems & Biodiversity
ICLR 2024 Scaling Transformers for Skillful and Reliable Medium-range Weather Forecasting (Papers Track) Overall Best Paper
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Abstract: Weather forecasting is a fundamental problem for anticipating and mitigating the impacts of climate change. Recently, data-driven approaches for weather forecasting based on deep learning have shown great promise, achieving accuracies that are competitive with operational systems. However, those methods often employ complex, customized architectures without sufficient ablation analysis, making it difficult to understand what truly contributes to their success. Here we introduce Stormer, a simple transformer model that achieves state-of-the-art performance on weather forecasting with minimal changes to the standard transformer backbone. We identify the key components of Stormer through careful empirical analyses, including weather-specific embedding, randomized dynamics forecast, and pressure-weighted loss. At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals. During inference, this allows us to produce multiple forecasts for a target lead time and combine them to obtain better forecast accuracy. On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days, while requiring orders-of-magnitude less training data and compute. Additionally, we demonstrate Stormer’s favorable scaling properties, showing consistent improvements in forecast accuracy with increases in model size and training tokens.

Authors: Tung Nguyen (University of California, Los Angeles); Rohan Shah (Carnegie Mellon University); Hritik Bansal (UCLA); Troy Arcomano (Argonne National Laboratory); Sandeep Madireddy (Argonne National Laboratory); Romit Maulik (Argonne National Laboratory); Veerabhadra Kotamarthi (Argonne National Laboratory); Ian Foster (Computation Institute); Aditya Grover (UCLA)

Climate Science & Modeling Extreme Weather
ICLR 2024 Categorization of Meteorological Data by Contrastive Clustering (Papers Track)
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Abstract: Visualized ceilometer backscattering data, displaying meteorological phenomena like clouds, precipitation, and aerosols, is mostly analyzed manually by meteorology experts. In this work, we present an approach for the categorization of backscattering data using a contrastive clustering approach, incorporating image and spatiotemporal information into the model. We show that our approach leads to meteorologically meaningful clusters, opening the door to the automatic categorization of ceilometer data, and how our work could potentially create insights in the field of climate science.

Authors: Michael Dammann (HAW Hamburg); Ina Mattis (DWD); Michael Neitzke (HAW Hamburg); Ralf Möller (University of Lübeck)

Unsupervised & Semi-Supervised Learning Climate Science & Modeling Computer Vision & Remote Sensing
ICLR 2024 AI-driven emulation of ocean dynamics on sub-seasonal scales (Papers Track)
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Abstract: Climate forecasting systems rely on coupling atmospheric models to ocean and sea ice models. However, while there have recently been significant efforts to accelerate atmospheric models using AI, there have been very scarce efforts to accelerate the latter. As a result, climate forecasting systems still rely on expensive numerical simulations, which renders large-scale ensembling and probabilistic prediction cumbersome. To address this issue, we propose a large-scale AI model of ocean dynamics. Our method relies on a spherical neural operator to accurately capture the functional nature of ocean dynamics on the sphere. We empirically demonstrate that our model can accurately predict ocean dynamics for sub-seasonal horizons and outperforms the existing method. It offers a 60x speedup over the fastest numerical solver currently used for the task.

Authors: Suyash Bire (Massachusetts Institute of Technology); Jean Kossaifi (NVIDIA); Simone Silvestri (Massachusetts Institute of Technology); Nikola Kovachki (Nvidia Corp.); Kamyar Azizzadenesheli (Nvidia Corp.); Chris N Hill (MIT); Animashree Anandkumar (Caltech)

Climate Science & Modeling Generative Modeling
ICLR 2024 Calibrating Bayesian UNet++ for Sub-seasonal Forecasting (Papers Track)
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Abstract: Seasonal forecasting is a crucial task when it comes to detecting the extreme heat and colds that occur due to climate change. Confidence in the predictions should be reliable since a small increase in the temperatures in a year has a big impact on the world. Calibration of the neural networks provides a way to ensure our confidence in the predictions. However, calibrating regression models is an under-researched topic, especially in forecasters. We calibrate a UNet++ based architecture, which was shown to outperform physics-based models in temperature anomalies. We show that with a slight trade-off between prediction error and calibration error, it is possible to get more reliable and sharper forecasts. We believe that calibration should be an important part of safety-critical machine learning applications such as weather forecasters.

Authors: Büşra Asan (Istanbul Technical University); Abdullah Akgül (University of Southern Denmark); Alper Unal (Istanbul Technical University); Melih Kandemir (University of Southern Denmark); Gozde Unal (Istanbul Technical University)

Uncertainty Quantification & Robustness Climate Science & Modeling
ICLR 2024 Extreme Precipitation Nowcasting using Transformer-based generative models (Papers Track)
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Abstract: This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, specifically VideoGPT with Extreme Value Loss (EVL) regularization. Leveraging a comprehensive dataset from the Royal Netherlands Meteorological Institute (KNMI), our study focuses on predicting short-term precipitation with high accuracy. We introduce a novel method for computing EVL without assuming fixed extreme representations, addressing the limitations of current models in capturing extreme weather events. We present both qualitative and quantitative analyses, demonstrating the superior performance of the proposed VideoGPT-EVL in generating accurate precipitation forecasts, especially when dealing with extreme precipitation events.

Authors: Cristian Meo (TUDelft); Mircea T Lica (Delft University of Technology); Ankush Roy (TUDelft); Zeina Boucher (TUDelft); Junzhe Yin (TUDelft); Yanbo Wang (Delft University of Technology); Ruben Imhoff (Deltares); Remko Uijlenhoet (TUDelft); Justin Dauwels (TU Delft)

Generative Modeling Climate Science & Modeling Computer Vision & Remote Sensing Time-series Analysis
ICLR 2024 GeoFormer: A Vision and Sequence Transformer-based Approach for Greenhouse Gas Monitoring (Papers Track)
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Abstract: Air pollution represents a pivotal environmental challenge globally, playing a major role in climate change via greenhouse gas emissions and negatively affecting the health of billions. However, predicting the spatial and temporal patterns of pollutants remains challenging. The scarcity of ground-based monitoring facilities and the dependency of air pollution modeling on comprehensive datasets, often inaccessible for numerous areas, complicate this issue. In this work, we introduce GeoFormer, a compact model that combines a vision transformer module with a highly efficient time-series transformer module to predict surface-level nitrogen dioxide (NO2) concentrations from Sentinel-5P satellite imagery. We train the proposed model to predict surface-level NO2 measurements using a dataset we constructed with Sentinel-5P images of ground-level monitoring stations, and their corresponding NO2concentration readings. The proposed model attains high accuracy (MAE 5.65), demonstrating the efficacy of combining vision and time-series transformer architectures to harness satellite-derived data for enhanced GHG emission insights, proving instrumental in advancing climate change monitoring and emission regulation efforts globally.

Authors: Madhav Khirwar (Independent)

Computer Vision & Remote Sensing Climate Science & Modeling Time-series Analysis
ICLR 2024 Paddy Doctor: Open Dataset and Automated Pest Identification Using Pre-trained Deep Learning Models (Papers Track)
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Abstract: Agriculture is one of the most important industries contributing to several countries’ national income. Timely identification of these pests is crucial for both farmers and agriculture experts to implement effective mitigation methods. Traditionally, farmers employ manual techniques based on their experience and visual inspection to identify paddy pests, but this is highly inefficient, time-consuming, and error-prone. At times, even experienced farmers and agriculture experts might struggle to identify a specific species of pests due to their size, colors similar to leaves, and the extensive variety of identical types. Moreover, farmers often apply a large quantity of pesticide without accurately identifying the exact type of pests and their underlying causes. Therefore, it is increasingly important to automate the process of detection of paddy pests to reduce pesticide usage and subsequently minimize the loss in yield. In this paper, we present an open dataset and deep learning models for automated pest identification in real paddy fields. Our dataset contains 6,062 annotated paddy leaf images across 17 classes (16 pest classes and a normal class). We benchmarked our dataset using six pre-trained models (ResNet34, VGG16, DenseNet121, EfficientNet_v2_m, MobileNet_V3_Large, and SqueezeNet1_0). The experimental results showed that ResNet34 achieved the highest accuracy of 98.31%. We release our dataset and reproducible code in the open source for community use.

Authors: Petchiammal A (Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu); Pandarasamy Arjunan (Indian Institute of Science, Bangalore)

Agriculture & Food
ICLR 2024 Using expired weather forecasts to supply 10 000y of data for accurate planning of a renewable European energy system (Papers Track)
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Abstract: Expanding renewable energy generation and electrifying heating to address climate change will heighten the exposure of our power systems to the variability of weather. Planning and assessing these future systems typically lean on past weather data. We spotlight the pitfalls of this approach---chiefly its reliance on what we claim is a limited weather record---and propose a novel approach: to evaluate these systems on two orders of magnitude more weather scenarios. By repurposing past ensemble weather predictions, we not only drastically expand the known weather distribution---notably its extreme tails---for traditional power system modeling but also unveil its potential to enable data-intensive self-supervised, diffusion-based and optimization ML techniques. Building on our methodology, we introduce a **dataset** collected from ECMWF ENS forecasts, encompassing power-system relevant variables over Europe, and detail the intricate process behind its assembly.

Authors: Petr Dolezal (AI4ER CDT, University of Cambridge); Emily Shuckburgh (University of Cambridge)

Power & Energy Climate Science & Modeling Data Mining Uncertainty Quantification & Robustness
ICLR 2024 Black carbon plumes from gas flaring in North Africa identified from multi-spectral imagery with deep learning (Papers Track)
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Abstract: Black carbon (BC) is an important pollutant aerosol emitted by numerous human activities, including gas flaring. Improper combustion in flaring activities can release large amounts of BC, which is harmful to human health and has a strong climate warming effect. To our knowledge, no study has ever directly monitored BC emissions from satellite imagery. Previous works quantified BC emissions indirectly, by applying emission coefficients to flaring volumes estimated from satellite imagery. Here, we develop a deep learning framework and apply it to Sentinel-2 imagery over North Africa during 2022 to detect and quantify BC emissions from gas flaring. We find that BC emissions in this region amount to about 1 million tCO2eq, or 1 million passenger cars, more than a quarter of which are due to 10 sites alone. This work demonstrates the operational monitoring of BC emissions from flaring, a key step in implementing effective mitigation policies to reduce the climate impact of oil and gas operations.

Authors: Alexandre Tuel (Galeio); Thomas Kerdreux (INRIA/ ENS); Louis THIRY (ENS Paris)

Earth Observation & Monitoring Computer Vision & Remote Sensing
ICLR 2024 Time-Varying Constraint-Aware Reinforcement Learning for Energy Storage Control (Papers Track)
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Abstract: Energy storage devices, such as batteries, thermal energy storages, and hydrogen systems, can help mitigate climate change by ensuring a more stable and sustainable power supply. To maximize the effectiveness of such energy storage, determining the appropriate charging and discharging amounts for each time period is crucial. Reinforcement learning is preferred over traditional optimization for the control of energy storage due to its ability to adapt to dynamic and complex environments. However, the continuous nature of charging and discharging levels in energy storage poses limitations for discrete reinforcement learning, and time-varying feasible charge-discharge range based on state of charge (SoC) variability also limits the conventional continuous reinforcement learning. In this paper, we propose a continuous reinforcement learning approach that takes into account the time-varying feasible charge-discharge range. An additional objective function was introduced for learning the feasible action range for each time period, supplementing the objectives of training the actor for policy learning and the critic for value learning. This actively promotes the utilization of energy storage by preventing them from getting stuck in suboptimal states, such as continuous full charging or discharging. This is achieved through the enforcement of the charging and discharging levels into the feasible action range. The experimental results demonstrated that the proposed method further maximized the effectiveness of energy storage by actively enhancing its utilization.

Authors: Jaeik Jeong (Electronics and Telecommunications Research Institute (ETRI)); Tai-Yeon Ku (Electronics and Telecommunications Research Institute (ETRI)); Wan-Ki Park (Electronics and Telecommunications Research Institute (ETRI))

Power & Energy Reinforcement Learning Time-series Analysis
ICLR 2024 Towards a Data-Driven Understanding of Cloud Structure Formation (Papers Track)
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Abstract: The physics of cloud formation and evolution is still not fully understood and constitutes one of the highest uncertainties in climate modeling. We are working on an approach that aims at improving our understanding of how clouds of different structures form from a data-driven perspective: By predicting the visual appearance of cloud photographs from physical quantities obtained from reanalysis data and subsequently attributing the decisions to physical quantities using ``explainable AI'' methods, we try to identify relevant physical processes. At the current stage, this is just a proof of concept, being at least able to identify basic meteorologically plausible facts from data.

Authors: Ann-Christin Wörl (Johannes Gutenberg University); Michael Wand (University of Mainz); Peter Spichtinger (Johannes Gutenberg University)

Interpretable ML Climate Science & Modeling
ICLR 2024 Bee Activity Prediction and Pattern Recognition in Environmental Data (Papers Track)
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Abstract: As a consequence of climate change, biodiversity is declining rapidly. Many species like insects, especially bees, suffer from changes in temperature and rainfall patterns. Applying machine learning for monitoring and predicting specie's health and life conditions can help understanding and improving biodiversity. In this work we use data collected from cameras and sensors mounted upon beehives together with different other data sources like weather data, information extracted from satellite images and geographical information. We aim at predicting bees' health (measured as their activity) and analyzing influencing environmental conditions. We show that we are able to accurately predict bees' activity and understand their life conditions by using machine learning algorithms and explainable AI. Understanding these conditions can help to make recommendations on good locations for beehives. This work illustrates the potential of applying machine learning on sensor, satellite and weather data for monitoring and predicting species' health and hence shows the ability for adaptation to climate change and a more accurate species monitoring.

Authors: Christine Preisach (University of Applied Sciences Karlsruhe); Marius Herrmann (Karlsruhe Institute of Technology)

Ecosystems & Biodiversity Interpretable ML
ICLR 2024 Advancing Earth System Model Calibration: A Diffusion-Based Method (Papers Track) Honorable Mention
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Abstract: Understanding of climate impact on ecosystems globally requires site-specific model calibration. Here we introduce a novel diffusion-based uncertainty quantification (DBUQ) method for efficient model calibration. DBUQ is a score-based diffusion model that leverages Monte Carlo simulation to estimate the score function and evaluates a simple neural network to quickly generate samples for approximating parameter posterior distributions. DBUQ is stable, efficient, and can effectively calibrate the model given diverse observations, thereby enabling rapid and site-specific model calibration on a global scale. This capability significantly advances Earth system modeling and our understanding of climate impacts on Earth systems. We demonstrate DBUQ's capability in E3SM land model calibration at the Missouri Ozark AmeriFlux forest site. Both synthetic and real-data applications indicate that DBUQ produces accurate parameter posterior distributions similar to those generated by Markov Chain Monte Carlo sampling but with 30X less computing time. This efficiency marks a significant stride in model calibration, paving the way for more effective and timely climate impact analyses.

Authors: Yanfang Liu (Oak Ridge National Laboratory); Dan Lu (Oak Ridge National Laboratory); Zezhong Zhang (Oak Ridge National Laboratory); Feng Bao (Florida State University); Guannan Zhang (Oak Ridge National Laboratory)

Generative Modeling Ecosystems & Biodiversity Uncertainty Quantification & Robustness
ICLR 2024 Towards Downscaling Global AOD with Machine Learning (Papers Track)
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Abstract: Poor air quality represents a significant threat to human health, especially in urban areas. To improve forecasts of air pollutant mass concentrations, there is a need for high-resolution Aerosol Optical Depth (AOD) forecasts as proxy. However, current General Circulation Model (GCM) forecasts of AOD suffer from limited spatial resolution, making it difficult to accurately represent the substantial variability exhibited by AOD at the local scale. To address this, a deep residual convolutional neural network (ResNet) is evaluated for the GCM to local scale downscaling of low-resolution global AOD retrievals, outperforming a non-trainable interpolation baseline. We explore the bias correction potential of our ResNet using global reanalysis data, evaluating it against in-situ AOD observations. The improved resolution from our ResNet can assist in the study of local AOD variations.

Authors: Josh Millar (Imperial College London); Paula Harder (Mila); Lilli J Freischem (University of Oxford); Philipp Weiss (University of Oxford); Philip Stier (University of Oxford)

Climate Science & Modeling Computer Vision & Remote Sensing
ICLR 2024 Reconstructing the Breathless Ocean with Spatio-Temporal Graph Learning (Papers Track)
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Abstract: The ocean is currently undergoing severe deoxygenation. Accurately reconstructing the breathless ocean is crucial for assessing and protecting marine ecosystem in response to climate change. Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and human activities. Besides, due to the high-cost data collection, the historical observations are severely sparse, leading to big challenge for precise reconstruction. In this work, we propose OxyGenerator, the first spatio-temporal graph learning model, to reconstruct the global ocean deoxygenation from 1920 to 2023. Specifically, to address the heterogeneity across large temporal and spatial scales, we propose zoning-varying graph message-passing to capture the complex oceanographic correlations between missing values and sparse observations. Additionally, to further calibrate the uncertainty, we incorporate inductive bias from dissolved oxygen (DO) variations and chemical effects. Compared with in-situ DO observations, OxyGenerator significantly outperforms CMIP6 numerical simulations, reducing MAPE by 38.77%, demonstrating a promising potential to understand the ocean deoxygenation in data-driven manner.

Authors: Bin Lu (Shanghai Jiao Tong University); Ze Zhao (Shanghai Jiao Tong University); Luyu Han (Shanghai Jiao Tong University); Xiaoying Gan (Shanghai Jiao Tong University); Yuntao Zhou (Shanghai Jiao Tong University); Lei Zhou (Shanghai Jiao Tong Univ); Luoyi Fu (Shanghai Jiao Tong University); Xinbing Wang (Shanghai Jiao Tong University); Chenghu Zhou (Institute of Geographic Sciences and Natural Resources Research, CAS); Jing Zhang (Shanghai Jiao Tong University)

Oceans & Marine Systems Data Mining Time-series Analysis
ICLR 2024 Analyzing the secondary wastewater-treatment process using Faster R-CNN and YOLOv5 object detection algorithms (Papers Track)
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Abstract: The activated sludge (AS) process is the most common type of secondary wastewater treatment, applied worldwide. Due to the complexity of microbial communities, imbalances between the different types of bacteria may occur and disturb the process, with pronounced economical and environmental consequences. Microscopic inspection of the morphology of flocs and microorganisms provides key information on AS properties and function. This is a time-consuming, highly skilled, and expensive process that is not readily available in all locations. Thus, most wastewater-treatment plants do not carry out this essential analysis, resulting in frequent operational faults. In this study, we develop a novel deep learning (DL) object detection algorithm to analyze and monitor the AS process based on a unique microscopic image database of flocs and microorganisms. Specifically, we applied YOLOv5 and Faster R-CNN algorithms as tools for segmentation and object detection to analyze the wastewater. The mean average precision (mAP) of the YOLOv5 was 0.67, outperforming the Faster R-CNN by 15%. Histogram equalization preprocessing of both bright-field and phase-contrast images significantly improved the results of the algorithm in all classes. In the case of YOLOv5, the mAP increased by 16.67%, to 0.77, where the AP of protozoa, filaments, and open floc classes outperformed the previous model by over 20%. These results demonstrate the potential of leveraging DL algorithms to enhance the analysis and monitoring of WWTPs in an affordable manner, consequently reducing environmental pollution caused by contaminated effluent. The fundamental challenge addressed herein has important global relevance, especially in an era in which the demand for high-quality wastewater reuse is expected to increase dramatically.

Authors: Offir Inbar (Tel-Aviv University); Moni Shahar (Tel Aviv University); Jacob Gidron (Tel-Aviv University); Ido Cohen (Tel-Aviv University); Dror Avisar (Tel-Aviv University)

Agriculture & Food Cities & Urban Planning Ecosystems & Biodiversity Health Chemistry & Materials Computer Vision & Remote Sensing
ICLR 2024 Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models (Papers Track)
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Abstract: Earth system models (ESMs) are the principal tools used in climate science to generate future climate projections under various atmospheric emissions scenarios on a global or regional scale. Generative deep learning approaches are suitable for emulating these tools due to their computational efficiency and ability, once trained, to generate realizations in a fraction of the time required by ESMs. We extend previous work that used a generative probabilistic diffusion model to emulate ESMs by targeting the joint emulation of multiple variables, temperature and precipitation, by a single diffusion model. Joint generation of multiple variables is critical to generate realistic samples of phenomena resulting from the interplay of multiple variables. The diffusion model emulator takes in the monthly mean-map of temperature and precipitation and produces the daily values of each of these variables that exhibit statistical properties similar to those generated by ESMs. Our results show the outputs from our extended model closely resemble those from ESMs on various climate metrics including dry spells and hot streaks, and that the joint distribution of temperature and precipitation in our sample closely matches those of ESMs.

Authors: Katie Christensen (Western Washington University); Lyric Otto (Western Washington University); Seth Bassetti (Utah State University); Claudia Tebaldi (Joint Global Change Research Institute); Brian Hutchinson (Western Washington University)

Climate Science & Modeling Generative Modeling
ICLR 2024 Verifying Practices of Regenerative Agriculture: African Smallholder Farmer Dataset for Remote Sensing and Machine Learning (Papers Track)
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Abstract: Despite Africa’s contribution to global greenhouse gas (GHG) emissions being only a few %, the continent experiences the harshest impacts, particularly within its food production systems. Regenerative agriculture is receiving a large amount of attention as a method to strengthen both food security and climate change resilience in Africa. For practicing regenerative agriculture, carbon credits are issued, but verifying the methodologies on a large scale is one of the challenging points in popularizing it. In this paper, we provide a comprehensive dataset on regenerative agriculture in sub-Saharan Africa. The dataset has field polygon information and is labeled with several types of regenerative agriculture methodologies. The dataset can be applied to local site analysis, classification, and detection of regenerative agriculture with remote sensing and machine learning. We also highlight several machine learning models and summarize the baseline results on our dataset. We believe that by providing this dataset, we can contribute to the establishment of verification methods for regenerative agriculture. The dataset can be downloaded from https://osf.io/xgp9m/.

Authors: Yohei Nakayama (Degas Ltd); Grace Antwi (Degas Ltd); Seiko Shirasaka (Keio University)

Agriculture & Food Computer Vision & Remote Sensing
ICLR 2024 Semi-Supervised Domain Adaptation for Wildfire Detection (Papers Track)
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Abstract: Recently, both the frequency and intensity of wildfires have increased worldwide, primarily due to climate change. In this paper, we propose a novel protocol for wildfire detection, leveraging semi-supervised Domain Adaptation for object detection, accompanied by a corresponding dataset designed for use by both academics and industries. Our dataset encompasses 30 times more diverse labeled scenes than the current largest benchmark wildfire dataset, HPWREN, and introduces a new labeling policy for wildfire detection. Inspired by coordconv., we propose a robust baseline, Location-Aware Object Detection for Semi-Supervised Domain Adaptation (LADA), utilizing a teacher-student based self-supervised Domain Adaptation framework capable of extracting translational variance features characteristic of wildfires. Our framework significantly outperforms the existing baseline by a notable margin of 3.8\%p in mean Average Precision on the HPWREN wildfire dataset.

Authors: Joo Young Jang (Alchera); Youngseo Cha (Alchera); Jisu Kim (Alchera); SooHyung Lee (Alchera); Geonu Lee (Alchera); Minkook Cho (Alchera); Young Hwang (Alchera); Nojun Kwak (Seoul National University)

Unsupervised & Semi-Supervised Learning Disaster Management and Relief Computer Vision & Remote Sensing
ICLR 2024 Identifying Climate Targets in National Laws and Policies using Machine Learning (Papers Track)
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Abstract: Quantified policy targets are a fundamental element of climate policy, typically characterised by domain-specific and technical language. Current methods for curating comprehensive views of global climate policy targets entail significant manual effort. At present there are few scalable methods for extracting climate targets from national laws or policies, which limits policymakers’ and researchers’ ability to (1) assess private and public sector alignment with global goals and (2) inform policy decisions. In this paper we present an approach for extracting mentions of climate targets from national laws and policies. We create an expert-annotated dataset identifying three categories of target (’Net Zero’, ’Reduction’ and ’Other’ (e.g. renewable energy targets)) and train a classifier to reliably identify them in text. We investigate bias and equity impacts related to our model and identify specific years and country names as problematic features. We explore the dataset generated from applying our classifier to the Climate Policy Radar (CPR) dataset, showcasing the potential for automated data collection and research support in climate policy. Our work represents a significant upgrade in the accessibility of these key climate policy elements for policymakers and researchers.

Authors: Matyas Juhasz (Climate Policy Radar); Tina Marchand (Climate Policy Radar); Roshan Melwani (Climate Policy Radar); Kalyan Dutia (Climate Policy Radar); Sarah Goodenough (Climate Policy Radar); Harrison Pim (Climate Policy Radar); Henry Franks (Climate Policy Radar)

Public Policy
ICLR 2024 Empowering Sustainable Finance: Leveraging Large Language Models for Climate-Aware Investments (Papers Track)
Abstract and authors: (click to expand)

Abstract: With the escalating urgency of climate change, it is becoming more imperative for businesses and organizations to align their objectives with sustainability goals. Financial institutions also face a critical mandate to fulfill the Sustainable Development Goals (SDGs), particularly goal 13, which targets the fight against climate change and its consequences. Mitigating the impacts of climate change requires a focus on reducing supply chain emissions, which constitute over 90% of total emission inventories. In the financial industry, supply chain emissions linked to lending and investments emerge as the primary source of emissions, posing challenges in tracking financed emissions due to the intricate process of collecting data from numerous suppliers across the supply chain. To address these challenges, we propose an emission estimation framework utilizing a Large Language Model (LLM) to drastically accelerate the assessment of the emissions associated with lending and investment activities. This framework utilizes financial activities as a proxy for measuring financed emissions. Utilizing the LLM, we classify financial activities into seven asset classes following the Partnership for Carbon Accounting Financials (PCAF) standard. Additionally, we map investments to industry categories and employ spend-based emission factors (kg-CO2/$-spend) to calculate emissions associated with financial investments. In our study, we compare the performance of our proposed method with state-of-the-art text classification models like TF-IDF, word2Vec, and Zero-shot learning. The results demonstrate that the LLM-based approach not only surpasses traditional text mining techniques and performs on par with a subject matter expert (SME) but most importantly accelerates the assessment process.

Authors: Ayush Jain (IBM Research); Manikandan Padmanaban (IBM Research India); Jagabondhu Hazra (IBM Research India); Shantanu Godbole (IBM India); Hendrik Hamann (IBM Research)

Natural Language Processing Supply Chains
ICLR 2024 Near-real-time monitoring of global ocean carbon sink (Papers Track)
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Abstract: The ocean, absorbing about 25% of anthropogenic CO2 emissions, plays a crucial role in mitigating climate change. However, the delayed (by one year) traditional estimates of ocean-atmosphere CO2 flux hinder timely understanding and response to the global carbon cycle’s dynamics. Addressing this challenge, we introduce Carbon Monitor Ocean (CMO-NRT), a pioneering dataset providing near-real-time, monthly gridded estimates of global surface ocean fugacity of CO2 (fCO2) and ocean-atmosphere CO2 flux from January 2022 to July 2023. This dataset marks a significant advancement by updating the global carbon budget’s estimates through a fusion of data from 10 Global Ocean Biogeochemical Models (GOBMs) and 8 data products into a near-real-time analysis framework. By harnessing the power of Convolutional Neural Networks (CNNs) and semi-supervised learning techniques, we decode the complex nonlinear relationships between model or product estimates and observed environmental predictors. The predictive models, both for GOBM and data products, exhibit exceptional accuracy, with root mean square errors (RMSEs) maintaining below the 5% threshold. This advancement supports more effective climate change mitigation efforts by providing scientists and policymakers with timely and accurate data.

Authors: Xiaofan Gui (Microsoft Research); Jiang Bian (Microsoft Research)

Climate Science & Modeling Unsupervised & Semi-Supervised Learning
ICLR 2024 WindDragon: enhancing wind power forecasting with automated deep learning (Papers Track)
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Abstract: Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids. This energy source poses a challenge to system operators due to its variability and uncertainty. Therefore, accurate forecasting of wind power is critical for grid operation and system balancing. This paper presents an innovative approach to short-term (1 to 6 hour horizon) wind power forecasting at a national level. The method leverages Automated Deep Learning combined with Numerical Weather Predictions wind speed maps to accurately forecast wind power.

Authors: Julie Keisler (INRIA, EDF R&D); Etienne Le Naour (Sorbonne University, EDF R&D)

Power & Energy
ICLR 2024 Neural Processes for Short-Term Forecasting of Weather Attributes (Papers Track)
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Abstract: Traditional weather prediction models rely on solving complex physical equations, with long computation time. Machine learning models can process large amount of data more quickly. We propose to use neural processes (NPs) for short-term weather attributes forecasting. This is a novel avenue of research, as previous work has focused on NPs for long-term forecasting. We compare a multi-task neural process (MTNP) to an ensemble of independent single-task NPs (STNP) and to an ensemble of Gaussian processes (GPs). We use time series data for multiple weather attributes from Chichester Harbour over a one-week period. We evaluate performance in terms of NLL and MSE with 2-hours and 6-hours time horizons. When limited context information is provided, the MTNP leverages inter-task knowledge and outperforms the STNP. The STNP outperforms both the MTNP and the GPs ensemble when a sufficient, but not exceeding, amount of context information is provided.

Authors: Benedetta L Mussati (University of Oxford); Helen McKay (Mind Foundry); Stephen Roberts (University of Oxford)

Time-series Analysis Meta- and Transfer Learning
ICLR 2024 EU Climate Change News Index: Forecasting EU ETS prices with online news (Papers Track)
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Abstract: Carbon emission allowance prices have been rapidly increasing in the EU since 2018 and accurate forecasting of EU Emissions Trading System (ETS) prices has become essential. This paper proposes a novel method to generate alternative predictors for daily ETS price returns using relevant online news information. We devise the EU Climate Change News Index by calculating the term frequency–inverse document frequency (TF–IDF) feature for climate change-related keywords. The index is capable of tracking the ongoing debate about climate change in the EU. Finally, we show that incorporating the index in a simple predictive model significantly improves forecasts of ETS price returns.

Authors: Aron Pap (BGSE); Aron D Hartvig (Corvinus University of Budapest, Cambridge Econometrics); Péter Pálos (Budapest University of Technology and Economics)

Climate Finance & Economics Power & Energy Public Policy Natural Language Processing Time-series Analysis
ICLR 2024 Explaining Zeolite Synthesis-Structure Relationships using Aggregated SHAP Analysis (Papers Track)
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Abstract: Zeolites, crystalline aluminosilicate materials with well-defined porous structures, have emerged as versatile materials with applications in carbon capture. Hydrothermal synthesis is a widely used method for zeolite production, offering control over crystallinity and and pore size. However, the intricate interplay of synthesis parameters necessitates a comprehensive understanding to optimize the synthesis process. We train a supervised classification machine learning model on ZeoSyn (a dataset of zeolite synthesis routes) to predict the zeolite framework product given a synthesis route. Subsequently, we leverage SHapley Additive Explanations (SHAP) to reveal key synthesis-structure relationships in zeolites. To that end, we introduce an aggregation SHAP approach to extend such analysis to explain the formation of composite building units (CBUs) of zeolites. Analysis at this unprecedented scale sheds light on key synthesis parameters driving zeolite crystallization.

Authors: Elton Pan (MIT)

Chemistry & Materials Carbon Capture & Sequestration Interpretable ML
ICLR 2024 Fast non-stationary geospatial modelling with multiresolution (wavelet) Gaussian processes (Papers Track)
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Abstract: Climate modelling tasks involve assimilating large amounts of geospatial data from different sources, such as simulators and measurements from weather stations and satellites. These sources of data are weighted according to their uncertainty, so good quality uncertainty estimates are essential. Gaussian processes (GPs) offer flexible models with uncertainty estimates, and have a long track record of use in geospatial modelling. However, much of the research effort, including recent work on scalability, is focused on statistically stationary models, which are not suitable for many climatic variables, such as precipitation. Here we propose a novel, scalable, nonstationary GP model based upon discrete wavelets, and evaluate them on toy and real world data.

Authors: Talay M Cheema (University of Cambridge); Carl Edward Rasmussen (Cambridge University)

Causal & Bayesian Methods Climate Science & Modeling Generative Modeling
ICLR 2024 PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting (Papers Track)
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Abstract: Accurate precipitation forecasting is a vital challenge of societal importance. Though data-driven approaches have emerged as a widely used solution, solely relying on data-driven approaches has limitations in modeling the underlying physics, making accurate predictions difficult. We focus on the Numerical Weather Prediction (NWP) post-processing based precipitation forecasting task to couple Machine Learning techniques with traditional NWP. This task remains challenging due to the imbalanced precipitation data and complex relationships between multiple meteorological variables. To address these limitations, we introduce the PostRainBench, a comprehensive multi-variable NWP post-processing benchmark, and CAMT, a simple yet effective Channel Attention Enhanced Multi-task Learning framework with a specially designed weighted loss function. Extensive experimental results on the proposed benchmark show that our method outperforms state-of-the-art methods by 6.3%, 4.7%, and 26.8% in rain CSI and improvements of 15.6%, 17.4%, and 31.8% over NWP predictions in heavy rain CSI on respective datasets. Most notably, our model is the first deep learning-based method to outperform NWP approaches in heavy rain conditions. These results highlight the potential impact of our model in reducing the severe consequences of extreme rainfall events. Our datasets and code are available at https://github.com/yyyujintang/PostRainBench.

Authors: Yujin Tang (The Hong Kong University of Science and Technology (Guangzhou)); Jiaming Zhou (Hong Kong University of Science and Technology (Guangzhou)); Xiang Pan (Nanjing University); Zeying Gong (Hong Kong University of Science and Technology (Guangzhou)); Junwei Liang (The Hong Kong University of Science and Technology (Guangzhou))

Extreme Weather Computer Vision & Remote Sensing
ICLR 2024 Physics-informed Machine Learning-based Cloud Microphysics parameterization for Earth System Models (Papers Track)
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Abstract: In this study, we develop a physics-informed machine learning (ML)-based cloud microphysics parameterization for the ICON model. By training the ML parameterization on high-resolution simulation data, we aim to improve Earth System Models (ESMs) in comparison to traditional parameterization schemes. We investigate the usage of a multilayer perceptron (MLP) with feature engineering and physics-constraints, and use explainability techniques to understand the relationship between input features and model output. Our novel approach yields promising results, with the physics-informed ML-based cloud microphysics parameterization achieving an R$^2$ score up to 0.777 for an individual feature. Additionally, we demonstrate a notable improvement in the overall performance in comparison to a baseline MLP, increasing its average R$^2$ score from 0.290 to 0.613 across all variables. This approach to improve the representation of cloud microphysics in ESMs promises to enhance climate projections, contributing to a better understanding of climate change.

Authors: Ellen Sarauer (German Aerospace Center (DLR)); Mierk Schwabe (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany); Philipp Weiss (University of Oxford); Axel Lauer (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany); Philip Stier (University of Oxford); Veronika Eyring (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany; University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany)

Climate Science & Modeling Interpretable ML
ICLR 2024 From spectra to biophysical insights: end-to-end learning with a biased radiative transfer model (Papers Track)
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Abstract: Advances in machine learning have boosted the use of Earth observation data for climate change research. Yet, the interpretability of machine-learned representations remains a challenge, particularly in understanding forests' biophysical reactions to climate change. Traditional methods in remote sensing that invert radiative transfer models (RTMs) to retrieve biophysical variables from spectral data often fail to account for biases inherent in the RTM, especially for complex forests. We propose to integrate RTMs into an auto-encoder architecture, creating an end-to-end learning approach. Our method not only corrects biases in RTMs but also outperforms traditional techniques for variable retrieval like neural network regression. Furthermore, our framework has potential generally for inverting biased physical models.

Authors: Yihang She (University of Cambridge); Clement Atzberger (Mantle Labs); Andrew Blake (University of Cambridge, Mantle Labs); Srinivasan Keshav (University of Cambridge)

Earth Observation & Monitoring Interpretable ML
ICLR 2024 Model Failure or Data Corruption? Exploring Inconsistencies in Building Energy Ratings with Self-Supervised Contrastive Learning (Papers Track)
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Abstract: Building Energy Rating (BER) stands as a pivotal metric, enabling building owners, policymakers, and urban planners to understand the energy-saving potential through improving building energy efficiency. As such, enhancing buildings' BER levels is expected to directly contribute to the reduction of carbon emissions and promote climate improvement. Nonetheless, the BER assessment process is vulnerable to missing and inaccurate measurements. In this study, we introduce CLEAR, a data-driven approach designed to scrutinize the inconsistencies in BER assessments through self-supervised contrastive learning. We validated the effectiveness of CLEAR using a dataset representing Irish building stocks. Our experiments uncovered evidence of inconsistent BER assessments, highlighting measurement data corruption within this real-world dataset.

Authors: Qian Xiao (Trinity College Dublin); Dan Liu (Trinity College Dublin); Kevin Credit (Maynooth University)

Buildings Data Mining Interpretable ML Unsupervised & Semi-Supervised Learning
ICLR 2024 Interpretable Machine Learning for Extreme Events detection: An application to droughts in the Po River Basin (Papers Track)
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Abstract: The increasing frequency and intensity of drought events-periods of significant decrease in water availability-are among the most alarming impacts of climate change. Monitoring and detecting these events is essential to mitigate their impact on our society. However, traditional drought indices often fail to accurately detect such impacts as they mostly focus on single precursors. In this study, we leverage machine learning algorithms to define a novel data-driven, impact-based drought index reproducing as target the Vegetation Health Index, a satellite signal that directly assesses the vegetation status. We first apply novel dimensionality reduction methods that allow for interpretable spatial aggregation of features related to precipitation, temperature, snow, and lakes. Then, we select the most informative and non-redundant features through filter feature selection. Finally, linear supervised learning methods are considered, given the small number of samples and the aim of preserving interpretability. The experimental setting focuses on ten sub-basins of the Po River basin, but the aim is to design a machine learning-based workflow applicable on a large scale.

Authors: Paolo Bonetti (Politecnico di Milano); Matteo Giuliani (Politecnico di Milano); Veronica Cardigliano (Politecnico di Milano); Alberto Maria Metelli (Politecnico di Milano); Marcello Restelli (Politecnico di Milano); Andrea Castelletti (Politecnico di Milano)

Agriculture & Food Extreme Weather Interpretable ML
ICLR 2024 Literature Mining with Large Language Models to Assist the Development of Sustainable Building Materials (Papers Track)
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Abstract: Concrete industry, as one of the significant sources of carbon emissions, drives the urgency for its decarbonization that requires a shift to alternative materials. However, the absence of systematic knowledge summary remains a challenge for further development of sustainable building materials. This work offers a cost-efficient strategy for information extraction tasks in complex terminology settings using small (2.8B) large language models (LLMs) with well-designed instruction-completion schemes and fine-tuning strategies, introducing a dataset cataloging civil engineering applications of alternative materials. The Multiple Choice instruction scheme significantly improves model accuracies in entity inference from non-Noun-Phrase sources, with supervised fine-tuning benefiting from straightforward tokenized representations of choices. We also demonstrate the utility of the dataset by extracting valuable insights into promising applications of alternative materials from knowledge graph representations.

Authors: Yifei Duan (Massachusetts Institute of Technology); Yixi Tian (Massachusetts Institute of Technology); Soumya Ghosh (IBM Research); Richard Goodwin (IBM T.J. Watson Research Center); Vineeth Venugopal (Massachusetts Institute of Technology); Jeremy Gregory (Massachusetts Institute of Technology); Jie Chen (IBM Research); Elsa Olivetti (Massachusetts Institute of Technology)

Natural Language Processing Buildings Chemistry & Materials
ICLR 2024 A Deep Learning Technology Suite for Cost-Effective Sequestered CO2 Monitoring (Papers Track)
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Abstract: Carbon capture and storage (CCS) is a way of reducing carbon emissions to help tackle global warming. Injecting CO2 into rock formations and preventing it from escaping to the surface is a main step in a CCS project. Therefore, monitoring of geologically sequestered CO2 is important for CCS security assessment. Time-lapse seismic (4D seismic) is one of the most effective tools for CO2 monitoring. Unfortunately, the main challenge of 4D seismic is the high cost due to repeated monitoring seismic data acquisition surveys and the subsequent time-consuming data processing that involves imaging and inversion. To address this, we developed a technology suite powered by deep learning engines that significantly reduces the cost by (1) acquiring very sparse monitoring data; (2) firing multiple seismic sources simultaneously; (3) converting 2D images to 3D volume; (4) enforcing repeatability between baseline data and monitoring data; and (5) nonlinearly mapping seismic data to subsurface property model to bypass complex wave-equation-based seismic data processing procedures.

Authors: Wenyi Hu (SLB); Son Phan (SLB); Cen Li (SLB); Aria Abubakar (SLB)

Carbon Capture & Sequestration Earth Observation & Monitoring Climate Science & Modeling Hybrid Physical Models Unsupervised & Semi-Supervised Learning
ICLR 2024 On the potential of Optimal Transport in Geospatial Data Science (Papers Track)
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Abstract: Prediction problems in geographic information science and transportation are often motivated by the possibility to enhance operational efficiency and thereby reduce emissions. Examples range from predicting car sharing demand for relocation planning to forecasting traffic congestion for navigation purposes. However, conventional accuracy metrics ignore the spatial distribution of the errors, despite its relevance for operations. Here, we put forward a spatially aware evaluation metric and loss function based on Optimal Transport (OT). Our framework leverages partial OT and can minimize relocation costs in any spatial prediction problem. We showcase the advantages of OT-based evaluation over conventional metrics and further demonstrate the application of an OT loss function for improving forecasts of bike sharing demand and charging station occupancy. Thus, our framework not only aligns with operational considerations, but also signifies a step forward in refining predictions within geospatial applications. All code is available at https://github.com/mie-lab/geospatial_optimal_transport.

Authors: Nina V Wiedemann (ETH Zurich); Martin Raubal (ETH Zürich)

Time-series Analysis Climate Science & Modeling Transportation
ICLR 2024 ExioML: Eco-economic dataset for Machine Learning in Global Sectoral Sustainability (Papers Track)
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Abstract: The Environmental Extended Multi-Regional Input-Output analysis is the predominant Ecological Economic research framework for analysing the environmental impact of economic activities. This paper introduces the novel ExioML dataset as the first Machine Learning benchmark data in sustainability analysis. We open-sourced the ExioML data and development toolkit to lower barriers and accelerate the cooperation between Machine Learning and Ecological Economic research. A crucial greenhouse gas emission regression task evaluates the usability of the proposed dataset. We compared the performance of traditional shallow models against deep models by leveraging a diverse factor accounting table and incorporating multiple modalities of categorical and numerical features. Our findings reveal that deep and ensemble models achieve low mean square errors below 0.25 and serve as a future machine learning research baseline. Through Ex- ioML, we aim to foster precise ML predictions and modelling to support climate actions and sustainable investment decisions. The data and codes are available: https://github.com/Yvnminc/ExioML

Authors: Yanming Guo (University of Sydney)

Climate Finance & Economics
ICLR 2024 FARADAY: SYNTHETIC SMART METER GENERATOR FOR THE SMART GRID (Papers Track)
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Abstract: Access to smart meter data is essential to rapid and successful transitions to elec- trified grids, underpinned by flexibility delivered by low carbon technologies, such as electric vehicles (EV) and heat pumps, and powered by renewable energy. Yet little of this data is available for research and modelling purposes due consumer privacy protections. Whilst many are calling for raw datasets to be unlocked through regulatory changes, we believe this approach will take too long. Synthetic data addresses these challenges directly by overcoming privacy issues. In this pa- per, we present Faraday, a Variational Auto-encoder (VAE)-based model trained over 300 million smart meter data readings from an energy supplier in the UK, with information such as property type and low carbon technologies (LCTs) own- ership. The model produces household-level synthetic load profiles conditioned on these labels, and we compare its outputs against actual substation readings to show how the model can be used for real-world applications by grid modellers interested in modelling energy grids of the future.

Authors: Sheng Chai (Centre for Net Zero); Gus Chadney (Centre for Net Zero)

Generative Modeling
ICLR 2024 Generalizable Temperature Nowcasting with Physics-Constrained RNNs for Predictive Maintenance of Wind Turbine Components (Papers Track)
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Abstract: Machine learning plays an important role in the operation of current wind energy production systems. One central application is predictive maintenance to increase efficiency and lower electricity costs by reducing downtimes. Integrating physics-based knowledge in neural networks to enforce their physical plausibilty is a promising method to improve current approaches, but incomplete system information often impedes their application in real world scenarios. We describe a simple and efficient way for physics-constrained deep learning-based predictive maintenance for wind turbine gearbox bearings with partial system knowledge. The approach is based on temperature nowcasting constrained by physics, where unknown system coefficients are treated as learnable neural network parameters. Results show improved generalization performance to unseen environments compared to a baseline neural network, which is especially important in low data scenarios often encountered in real-world applications.

Authors: Johannes Exenberger (Graz University of Technology); Matteo Di Salvo (Sirius Energy Automation); Thomas Hirsch (Graz University of Technology); Franz Wotawa (Graz University of Technology, Institute for Software Technology); Gerald Schweiger (Graz University of Technology)

Hybrid Physical Models Time-series Analysis
ICLR 2024 Forecasting Tropical Cyclones with Cascaded Diffusion Models (Papers Track)
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Abstract: As tropical cyclones become more intense due to climate change, the rise of Al-based modelling provides a more affordable and accessible approach compared to traditional methods based on mathematical models. This work leverages generative diffusion models to forecast cyclone trajectories and precipitation patterns by integrating satellite imaging, remote sensing, and atmospheric data. It employs a cascaded approach that incorporates three main tasks: forecasting, super-resolution, and precipitation modelling. The training dataset includes 51 cyclones from six major tropical cyclone basins from January 2019 - March 2023. Experiments demonstrate that the final forecasts from the cascaded models show accurate predictions up to a 36-hour rollout, with excellent Structural Similarity (SSIM) and Peak-Signal-To-Noise Ratio (PSNR) values exceeding 0.5 and 20 dB, respectively, for all three tasks. The 36-hour forecasts can be produced in as little as 30 mins on a single Nvidia A30/RTX 2080 Ti. This work also highlights the promising efficiency of Al methods such as diffusion models for high-performance needs in weather forecasting, such as tropical cyclone forecasting, while remaining computationally affordable, making them ideal for highly vulnerable regions with critical forecasting needs and financial limitations. Code accessible at \url{https://github.com/nathzi1505/forecast-diffmodels}.

Authors: Pritthijit Nath (Imperial College London); Pancham Shukla (Imperial College London); Shuai Wang (University of Delaware); Cesar Quilodran-Casas (Imperial College London)

Generative Modeling Extreme Weather
ICLR 2024 Forecasting regional PV power in Great Britain with a multi-modal late fusion network (Papers Track)
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Abstract: The ability to forecast solar photovoltaic (PV) power is important for grid balancing and reducing the CO2 intensity of electricity globally. The use of multi-modal data such as numerical weather predictions (NWPs) and satellite imagery can be harnessed to make more accurate PV forecasts. In this work, we propose a late fusion model which integrates two different NWP sources alongside satellite images to make 0-8 hour lead time forecasts for grid regions across Great Britain. We limit the model inputs to be reflective of those available in a live production system. We show how the different input data sources contribute to average error at each time horizon and compare against a simple baseline.

Authors: James Fulton (Open Climate Fix); Jacob Bieker (Open Climate Fix); Peter Dudfield (Open Climate Fix); Solomon Cotton (Open Climate Fix); Zakari Watts (Open Climate Fix); Jack Kelly (Open Climate Fix)

Power & Energy Computer Vision & Remote Sensing
ICLR 2024 Exploring Graph Neural Networks to Predict the Seagrasses Ecosystem State in the Italian Seas (Papers Track)
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Abstract: Marine coastal ecosystems (MCEs) play a critical role in climate change adaptation and human well-being. However, they face global threats from environmental pressures, both related to climate change (CC) and direct human impacts. Leveraging the increasing availability of geospatial data, this study explores Graph Neural Networks (GNNs) to assess cumulative impacts arising from human and CC related pressures on the Seagrass ecosystem in the Italian seas. Unlike traditional machine learning (ML) models with which they were compared in this study, GNNs incorporate the spatial component of data through graph structures. While experimental results demonstrate a modest performance improvement in GNNs, the study is constrained by limited data availability, preventing the exploration of the temporal component and physical laws representable through graph structures. Future efforts aim to collect higher-resolution spatial and temporal data, considering expressible environmental processes, to enhance model learning.

Authors: Angelica Bianconi (University School for Advanced Studies (IUSS) Pavia & Ca’ Foscari University of Venice); Sebastiano Vascon (Ca' Foscari University of Venice & European Centre for Living Technology); Elisa Furlan (Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) & Ca' Foscari University of Venice); Andrea Critto (Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC) & Ca' Foscari University of Venice)

Ecosystems & Biodiversity Oceans & Marine Systems
ICLR 2024 Global High Resolution CO2 monitoring using Super Resolution (Papers Track)
Abstract and authors: (click to expand)

Abstract: Monitoring Greenhouse Gases (GHGs) concentrations and emissions is essential to mitigate climate change. Thanks to the large amount of satellite data available, it is now possible to understand GHGs' behaviours at a broad scale. However, due to remote sensing devices technological limitations, the task of global high resolution (HR) monitoring remains an open problem. To avoid waiting for new missions and better data to be generated, it is therefore relevant to experiment with processing methods able to improve existing datasets. Our paper proposes to apply Super Resolution (SR), a Deep Learning (DL) approach commonly used in Computer Vision (CV), on global L3 satellite data. We produce a daily high resolution global CO2 dataset that opens the door for globally consistent point source monitoring.

Authors: Andrianirina Rakotoharisoa (Imperial College London); Rossella Arcucci (Imperial College London)

Computer Vision & Remote Sensing Earth Observation & Monitoring
ICLR 2024 DiffObs: Generative Diffusion for Global Forecasting of Satellite Observations (Papers Track)
Abstract and authors: (click to expand)

Abstract: This work presents an autoregressive generative diffusion model (DiffObs) to predict the global evolution of daily precipitation, trained on a satellite observational product, and assessed with domain-specific diagnostics. The model is trained to probabilistically forecast day-ahead precipitation. Nonetheless, it is stable for multi-month rollouts, which reveal a qualitatively realistic superposition of convectively coupled wave modes in the tropics. Cross-spectral analysis confirms successful generation of low frequency variations associated with the Madden--Julian oscillation, which regulates most subseasonal to seasonal predictability in the observed atmosphere, and convectively coupled moist Kelvin waves with approximately correct dispersion relationships. Despite secondary issues and biases, the results affirm the potential for a next generation of global diffusion models trained on increasingly sparse, and increasingly direct and differentiated observations of the world, for practical applications in subseasonal and climate prediction.

Authors: Jason Stock (Colorado State University); Jaideep Pathak (NVIDIA Corporation); Yair Cohen (NVIDIA Corporation); Mike Pritchard (NVIDIA Corporation); Piyush Garg (NVIDIA); Dale Durran (NVIDIA Corporation); Morteza Mardani (NVIDIA Corporation); Noah D Brenowitz (NVIDIA)

Climate Science & Modeling Computer Vision & Remote Sensing Generative Modeling
ICLR 2024 A cautionary tale about deep learning-based climate emulators (Papers Track)
Abstract and authors: (click to expand)

Abstract: Climate models are computationally too expensive for many tasks, such as, rapidly exploring future impacts of climate policies. Thus, since the 1980s scientists have been developing lightweight approximations or emulators of climate models. Recently, deep learning has been proposed for this task and most commonly been evaluated on the benchmark ClimateBenchv1.0. We implemented a linear regression-based model from the 1990s with 30K parameters, called linear pattern scaling, that is now the 'best' model on ClimateBenchv1.0 -- outperforming the incumbent 100M-parameter foundation model, ClimaX, on the spatial error of 3 out of the 4 variables. Nevertheless, climate emulation might benefit from innovations in machine learning and we analyse two aspects that need to be addressed in future emulators: First, the data complexity depends strongly on the climate variable of interest and the chosen spatiotemporal resolution. Second, current benchmarks do not sufficiently address the large impact of interannual variability in the climate system. We have published our analysis as an interactive tutorial at github.com/ygaxolotl/tags-linear-pattern-scaling.

Authors: Björn Lütjens (Massachussets Institute of Technology); Raffaele Ferrari (Massachusetts Institute of Technology); Paolo Giani (Massachusetts Institute of Technology); Dava Newman (MIT); Andre Souza (Massachusetts Institute of Technology); Duncan Watson-Parris (University of California San Diego); Noelle Selin (Massachusetts Institute of Technology)

Climate Science & Modeling Hybrid Physical Models Time-series Analysis
ICLR 2024 CausalPrompt: Enhancing LLMs with Weakly Supervised Causal Reasoning for Robust Performance in Non-Language Tasks (Papers Track)
Abstract and authors: (click to expand)

Abstract: In confronting the pressing issue of climate change, we introduce "CausalPrompt", an innovative prompting strategy that adapts large language models (LLMs) for classification and regression tasks through the application of weakly supervised causal reasoning. We delve into the complexities of data shifts within energy systems, often resulting from the dynamic evolution of sensor networks, leading to discrepancies between training and test data distributions or feature inconsistencies. By embedding domain-specific reasoning in the finetuning process, CausalPrompt significantly bolsters the adaptability and resilience of energy systems to these shifts. We show that CausalPrompt significantly enhances predictions in scenarios characterized by feature shifts, including electricity demand, solar power generation, and cybersecurity within energy infrastructures. This approach underlines the crucial role of CausalPrompt in enhancing the reliability and precision of predictions in energy systems amid feature shifts, highlighting its significance and potential for real-world applications in energy management and cybersecurity, contributing effectively to climate change mitigation efforts.

Authors: Tung-Wei Lin (University of California, Berkeley); Vanshaj Khattar (Virginia Tech); Yuxuan Huang (University College London); Junho Hong (University of Michigan); Ruoxi Jia (Virginia Tech); Chen-Ching Liu (Virginia Tech); Alberto L Sangiovanni-Vincentelli (University of California, Berkeley); Ming Jin (Virginia Tech)

Natural Language Processing Buildings Power & Energy
ICLR 2024 Estimating the age of buildings from satellite and morphological features to create a pan-EU Digital Building Stock Model (Papers Track)
Abstract and authors: (click to expand)

Abstract: The acceleration in the effects of global warming and the recent turbulences in the energy market are further highlighting the need to act quicker and smarter in terms of decisions to transition to greener energy and reduce our overall energy consumption. With buildings accounting for about 40% of the energy consumption in Europe, it is crucial to have a comprehensive understanding of the building stock and their energy-related characteristics, including their age, in order to make informed decisions for energy savings. This study introduces a novel way to approach the age estimation of buildings at scale, using a machine learning method that integrates satellite-based imagery with morphological features of buildings. The findings demonstrate the benefits of combining these data sources and underscore the importance of incorporating local data to enable accurate prediction across different cities.

Authors: Jeremias Wenzel (Universiteit Twente); Ana M. Martinez (European Commission - Joint Research Centre); Pietro Florio (European Commission - Joint Research Centre); Katarzyna Goch (Institute of Geography and Spatial Organization Polish Academy of Sciences)

Buildings Cities & Urban Planning
ICLR 2024 Probabilistic electricity price forecasting through conformalized deep ensembles (Papers Track)
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Abstract: Probabilistic electricity price forecasting (PEPF) is subject of an increasing interest, following the demand for proper prediction uncertainty quantification, to support the operation in complex power markets with increasing share of renewable generation. Distributional neural networks ensembles (DE) have been recently shown to outperform state of the art PEPF benchmarks. Still, they require reliability improvements, as fail to pass the coverage tests at various steps on the prediction horizon. In this work, we tackle this issue by extending the DE framework with the introduction of a Conformal Prediction based technique. Experiments have been conducted on multiple market regions, achieving day-ahead probabilistic forecasts with better hourly coverage.

Authors: Alessandro Brusaferri (National Research Council of Italy); Andrea Ballarino (National Research Council of Italy); Luigi Grossi (University of Parma); Fabrizio Laurini (University of Parma)

Power & Energy
ICLR 2024 Interpretable Machine Learning for power systems: Establishing Confidence in SHapley Additive exPlanationS (Papers Track)
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Abstract: Interpretable Machine Learning (IML) is expected to remove significant barriers for the application of Machine Learning (ML) algorithms in power systems. This work first seeks to showcase the benefits of SHapley Additive exPlanations (SHAP) for understanding the outcomes of ML models, which are increasingly being used to optimise power systems with increasing share of Renewable Energy (RE), to support worldwide calls for decarbonisation and climate change. To do so, we demonstrate that the Power Transfer Distribution Factors (PTDF)—a power system physics-based linear sensitivity index—can be derived from the SHAP values. To do so, we take the derivatives of SHAP values from a ML model trained to learn line-flows from generator power-injections, using a DC power-flow case in a benchmark test network. In demonstrating that SHAP values can be related back to the physics that underpin the power system, we build confidence in the explanations SHAP can offer.

Authors: Tabia Ahmad (University of Strathclyde); Robert Hamilton (Shell); Panagiotis Papadopoulos (University of Manchester); Samuel Chevalier (University of Vermont); Ilgiz Murzakhanov (Technical University of Denmark); Rahul Nellikkath (Technical University of Denmark); Jochen Bernhard Stiasny (Technical University of Denmark); Spyros Chatzivasileiadis (Technical University of Denmark)

Power & Energy Interpretable ML
ICLR 2024 DETECTION OF METEOROLOGICAL VARIABLES IN A WIND FARM INFLUENCING THE EXTREME WIND SPEED BY HETEROGENEOUS GRANGER CAUSALITY (Papers Track)
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Abstract: For an efficiently managed wind farm and wind power generation under adverse weather, knowledge of meteorological parameters influencing wind speed is of crucial importance for optimized and improved forecasts. We investigate temporal effects of wind speed related processes such as wakes within the wind farm using the Heterogeneous Graphical Granger model. The ERA5 meteorological reanalysis was used to generate wind farm power production data in Eastern Austria. We evaluated six different scenarios for the hydrological half-year period, based on moderate wind speed and varying temporal intervals of low or high extreme wind speed This allows to carry out causal reasoning about possible causes of extreme wind speed in a wind farm. A set of causal parameters for each of the scenarios was discovered enabling future early warning and for taking management measures for wind farm power generation management under adverse weather conditions.

Authors: Katerina Schindlerova (UniVie); Irene Schicker (Geos); Kejsi Hoxhallari (UniVie); Claudia Plant (University of Vienna, Austria)

Causal & Bayesian Methods Extreme Weather
ICLR 2024 Machine Learning for the Detection of Arctic Melt Ponds from Infrared Imagery (Papers Track)
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Abstract: Melt ponds are pools of water on Arctic summer sea ice that play an important role in the Arctic climate system. Retrieving their coverage is essential to better understand and predict the rapidly changing Arctic, but current data are limited. The goal of this study is to enhance melt pond data by developing a method that segments thermal infrared (TIR) airborne imagery into melt pond, sea ice, and ocean classes. Due to temporally and spatially varying surface temperatures, we use a data-driven deep learning approach to solve this task. We adapt and fine-tune AutoSAM, a Segment Anything-based segmentation model. We make the code, data, and models available online.

Authors: Marlena Reil (University of Osnabrück and University of Bremen, Institute of Environmental Physics); Gunnar Spreen (University of Bremen, Institute of Environmental Physics); Marcus Huntemann (University of Bremen, Institute of Environmental Physics); Lena Buth (Alfred Wegener Institute); Dennis Wilson (University of Toulouse, ISAE-Supaero)

Earth Observation & Monitoring Computer Vision & Remote Sensing
ICLR 2024 Identifying Complex Dynamics of Power Grid Frequency (Papers Track)
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Abstract: The energy system is undergoing rapid changes to integrate a growing number of intermittent renewable generators and facilitate the broader transition toward sustainability. As millions of consumers and thousands of (volatile) generators are connected to the same synchronous grid, no straightforward bottom-up models describing the dynamics are available on a continental scale comprising all of these necessary details. Hence, to identify this unknown power grid dynamics, we propose to leverage the Sparse Identification of Nonlinear Dynamics (SINDy) method. Thereby, we unveil the governing equations underlying the dynamical system directly from data measurements. Investigating the power grids of Iceland, Ireland and the Balearic islands as sample systems, we observe structurally similar dynamics with remarkable differences in both quantitative and qualitative behavior. Overall, we demonstrate how complex, i.e. non-linear, noisy, and time-dependent, dynamics can be identified straightforwardly.

Authors: Xinyi Wen (Karlsruhe Institute of Technology); Ulrich Oberhofer (Karlsruhe Institute of Technology); Leonardo Rydin Gorjão (Norwegian University of Life Sciences); G.Cigdem YALCIN (Istanbul University); Veit Hagenmeyer (Karlsruhe Institute of Technology (KIT)); Benjamin Schäfer (Karlsruhe Institute of Technology)

Power & Energy Time-series Analysis
ICLR 2024 Imbalance-aware Presence-only Loss Function for Species Distribution Modeling (Papers Track)
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Abstract: In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting environmental conditions to species occurrences. Traditionally limited by a scarcity of species observations, these models have significantly improved in performance through the integration of larger datasets provided by citizen science initiatives. However, they still suffer from the strong class imbalance between species within these datasets, often resulting in the penalization of rare species--those most critical for conservation efforts. To tackle this issue, this study assesses the effectiveness of training deep learning models using a balanced presence-only loss function on various large citizen science-based datasets. We demonstrate that this imbalance-aware loss function outperforms traditional loss functions across various datasets and tasks, particularly in accurately modeling rare species with limited observations.

Authors: Robin Zbinden (EPFL); Nina van Tiel (EPFL); Marc Rußwurm (Wageningen University); Devis Tuia (EPFL)

Ecosystems & Biodiversity
ICLR 2024 Deep Gaussian Processes and inversion for decision support in model-based climate change mitigation and adaptation problems (Papers Track)
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Abstract: To inform their decisions, policy makers often rely on models developed by researchers that are computationally intensive and complex and that frequently run on High Performance Computers (HPC). These decision-support models are not used directly by deciders and the results of these models tend to be presented by experts as a limited number of potential scenarios that would result from a limited number of potential policy choices. Machine learning models such as Deep Gaussian Processes (DGPs) can be used to radically re-define how decision makers can use models by creating a ‘surrogate model’ or ‘emulator’ of the original model. Surrogate models can then be embedded into apps that decisions makers can use to directly explore a vast array of policy options corresponding to potential target outcomes (model inversion). To illustrate the mechanism, we give an example of application that is envisaged as part of the UK government’s Net Zero strategy. To achieve Net Zero CO2 emissions by 2050, the UK government is considering multiple options that include planting trees to capture carbon. However, the amount of CO2 captured by the trees depend on a large number of factors that include climate conditions, soil type, soil carbon, tree type, ... Depending on these factors the net balance of carbon removal after planting trees may not necessarily be positive. Hence, choosing the right place to plant the right tree is very important. A decision-helping model has been developed to tackle this problem. For a given policy input, the model outputs its impact in terms of CO2 sequestration, biodiversity and other ecosystem services. We show how DGPs can be used to create a surrogate model of this original afforestation model and how these can be embedded into an R shiny app that can then be directly used by decision makers.

Authors: bertrand nortier (University of Exeter); daniel williamson (University of Exeter); mattia mancini (University of Exeter); amy binner (University of Exeter); brett day (University of Exeter); ian bateman (University of Exeter)

Public Policy Carbon Capture & Sequestration Ecosystems & Biodiversity Forests Causal & Bayesian Methods Uncertainty Quantification & Robustness
ICLR 2024 Generalized Policy Learning for Smart Grids: FL TRPO Approach (Papers Track)
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Abstract: The smart grid domain requires bolstering the capabilities of existing energy management systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable ability to train models on heterogeneous datasets while maintaining data privacy, making it suitable for smart grid applications, which often involve disparate data distributions and interdependencies among features that hinder the suitability of linear models. This paper introduces a framework that combines FL with a Trust Region Policy Optimization (FL TRPO) aiming to reduce energy-associated emissions and costs. Our approach reveals latent interconnections and employs personalized encoding methods to capture unique insights, understanding the relationships between features and optimal strategies, allowing our model to generalize to previously unseen data. Experimental results validate the robustness of our approach, affirming its proficiency in effectively learning policy models for smart grid challenges.

Authors: Yunxiang LI (MBZUAI); Nicolas M Cuadrado (MBZUAI); Samuel Horváth (MBZUAI); Martin Takac (Mohamed bin Zayed University of Artificial Intelligence)

Power & Energy Reinforcement Learning
ICLR 2024 A Deep Learning Framework to Efficiently Estimate Precipitation at the Convection Permitting Scale (Papers Track)
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Abstract: Precipitation-related extreme events are rapidly growing due to climate change, emphasizing the need for accurate hazard projections. To effectively model the convective phenomena driving severe precipitation, high-resolution estimates are crucial. Existing methods struggle with either insufficient expressiveness in capturing complex convective dynamics, due to the low resolution, or excessive computational demands. In response, we propose an innovative deep learning framework that efficiently harnesses available data to yield precise results. This model, based on graph neural networks, utilises two grids with different resolution and two sets of edges to represent spatial relationships. Employing as input ERA5 reanalysis atmospheric variables on an approximately 25 km grid, the framework produces hourly precipitation estimates on a finer 3 km grid. Findings are promising in accurately capturing yearly precipitation distribution and estimating cumulative precipitation during extreme events. Notably, the model demonstrates effectiveness in spatial regions not included in the training, motivating further exploration of its transferability potential.

Authors: Valentina Blasone (University of Trieste); Erika Coppola (Earth System Physics Section, ICTP, Trieste); Guido Sanguinetti (SISSA); Viplove Arora (Theoretical and Scientific Data Science, SISSA, Trieste); Serafina Di Gioia (Earth System Physics Section, ICTP, Trieste); Luca Bortolussi (University of Trieste)

Climate Science & Modeling Extreme Weather
ICLR 2024 Global Vegetation Modeling With Pre-Trained Weather Transformers (Papers Track)
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Abstract: Accurate vegetation models can produce further insights into the complex inter-action between vegetation activity and ecosystem processes. Previous research has established that long-term trends and short-term variability of temperature and precipitation affect vegetation activity. Motivated by the recent success of Transformer-based Deep Learning models for medium-range weather forecasting, we adapt the publicly available pre-trained FourCastNet to model vegetation activity while accounting for the short-term dynamics of climate variability. We investigate how the learned global representation of the atmosphere’s state can be transferred to model the normalized difference vegetation index (NDVI). Our model globally estimates vegetation activity at a resolution of 0.25◦ while relying only on meteorological data. We demonstrate that leveraging pre-trained weather models improves the NDVI estimates compared to learning an NDVI model from scratch. Additionally, we compare our results to other recent data-driven NDVI modeling approaches from machine learning and ecology literature. We further provide experimental evidence on how much data and training time is necessary to turn FourCastNet into an effective vegetation model. Code and models are available at https://github.com/LSX-UniWue/Global-Ecosystem-Modeling.

Authors: Pascal Janetzky (University Wuerzburg); Florian Gallusser (Universität Würzburg); Simon Hentschel (Julius-Maximilians-Universität of Würzburg); Andreas Hotho (University of Wuerzburg); Anna Krause (Universität Würzburg, Department of Computer Science, CHair X Data Science)

Ecosystems & Biodiversity Climate Science & Modeling Data Mining
ICLR 2024 Building Ocean Climate Emulators (Papers Track)
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Abstract: The current explosion in machine learning for climate has led to skilled, computationally cheap emulators for the atmosphere. However, the research for ocean emulators remains nascent despite the large potential for accelerating coupled climate simulations and improving ocean forecasts on all timescales. There are several fundamental questions to address that can facilitate the creation of ocean emulators. Here we focus on two questions: 1) the role of the atmosphere in improving the extended skill of the emulator and 2) the representation of variables with distinct timescales (e.g., velocity and temperature) in the design of any emulator. In tackling these questions, we show stable prediction of surface fields for over 8 years, training and testing on data from a high-resolution coupled climate model, using results from four regions of the globe. Our work lays out a set of physically motivated guidelines for building ocean climate emulators.

Authors: Adam Subel (New York University); Laure Zanna (New York University)

Climate Science & Modeling
ICLR 2024 Neural Tree Reconstruction for the Open Forest Observatory (Papers Track)
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Abstract: The Open Forest Observatory (OFO) is a collaboration across universities and other partners to make low-cost forest mapping accessible to ecologists, land managers, and the general public. The OFO is building both a database of geospatial forest data as well as open-source methods and tools for forest mapping by un- crewed aerial vehicle. Such data are useful for a variety of climate applications including prioritizing reforestation efforts, informing wildfire hazard reduction, and monitoring carbon sequestration. In the current iteration of the OFO’s forest map database, 3D tree maps are created using classical structure-from-motion techniques. This approach is prone to artifacts, lacks detail, and has particular difficulty on the forest floor where the input data (overhead imagery) has limited visibility. These reconstruction errors can potentially propagate to the down- stream scientific tasks (e.g. a wildfire simulation.) Advances in 3D reconstruction, including methods like Neural Radiance Fields (NeRF), produce higher quality results that are more robust to sparse views and support data-driven priors. We explore ways to incorporate NeRFs into the OFO dataset, outline future work to support even more state-of-the-art 3D vision models, and describe the importance of high-quality 3D reconstructions for forestry applications.

Authors: Marissa Ramirez de Chanlatte (UC Berkeley); Arjun Rewari (Darrell Group, Berkeley AI Research Lab); Trevor Darrell (UC Berkeley); Derek Young (University of California Davis)

Forests Carbon Capture & Sequestration
ICLR 2024 Graph Neural Network Based Screening of Metal-Organic Frameworks for CO2 Capture (Papers Track)
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Abstract: Our ability to capture and remove carbon dioxide (CO2) at gigaton scale within a decade or two depends on our ability to quickly identify new materials that are high performing, selective over other gases with low energy demand and then further develop them for large scale deployment. As a proven technology for gas separation in other industrial applications, metal-organic frameworks (MOF) come in virtually unlimited number of crystal combinations in their highly porous lattice and may offer the solution for CO2 capture from atmosphere or industrial point sources. Although MOFs can have highly complex crystal structure, which cannot be easily exploited in tabular data format in conventional ML methods or more recent Deep Learning methods, Graph Neural Networks can easily be trained on their representative crystallographic information file (CIF) content. In this work, we train GNNs to create an end-to-end workflow to screen large number of MOF crystal structures directly from the data within the crystallographic information files for their CO2 working capacity or CO2/N2 selectivity under low-pressure conditions. Our preliminary results show that a simple 2-layered Graph Convolution Networks (GCN) can easily achieve R2 score in the range of 0.87 to 0.89, easily.

Authors: Zikri Bayraktar (Schlumberger Doll Research); Mengying Li (Schlumberger Doll Research); Shahnawaz Molla (Schlumberger Doll Research)

Carbon Capture & Sequestration
ICLR 2024 Predicting Species Occurrence Patterns from Partial Observations (Papers Track)
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Abstract: To address the interlinked biodiversity and climate crises, we need an understanding of where species occur and how these patterns are changing. However, observational data on most species remains very limited, and the amount of data available varies greatly between taxonomic groups. We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies, which is designed to pair with the existing SatBird dataset of bird observational data. To address this task, we propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found. We find that R-Tran outperforms other methods in predicting species encounter rates with partial information both within a taxon (birds) and across taxa (birds and butterflies). Our approach opens new perspectives to leveraging insights from species with abundant data to other species with scarce data, by modelling the ecosystems in which they co-occur.

Authors: Hager Radi Abdelwahed (Mila: Quebec AI Institute); Mélisande Teng (Mila, Université de Montréal); David Rolnick (MIT)

Ecosystems & Biodiversity Computer Vision & Remote Sensing
ICLR 2024 Towards Scalable Deep Species Distribution Modelling using Global Remote Sensing (Papers Track)
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Abstract: Destruction of natural habitats and anthropogenic climate change are threatening biodiversity globally. Addressing this loss necessitates enhanced monitoring techniques to assess the impact of environmental shifts and to guide policy-making efforts. Species distribution models are crucial tools that predict species locations by interpolating observed field data with environmental information. We develop an improved, scalable method for species distribution modelling by proposing a dataset pipeline that incorporates global remote sensing imagery, land use classification data, environmental variables, and observation data, and utilising this with convolutional neural network (CNN) models to predict species presence at higher spatial and temporal resolutions than well-established species distribution modelling methods. We apply our approach to modelling Protea species distributions in the Cape Floristic Region of South Africa, demonstrating its performance in a region of high biodiversity. We train two CNN models and compare their performance to Maxent, a popular conventional species distribution modelling method. We find that the CNN models trained with remote sensing data outperform Maxent, underscoring the potential of our method as an effective and scalable solution for modelling species distribution.

Authors: Emily Morris (University of Cambridge); Anil Madhavapeddy (University of Cambridge); Sadiq Jaffer (University of Cambridge); David Coomes (University of Cambridge)

Ecosystems & Biodiversity Computer Vision & Remote Sensing
ICLR 2024 Valuation and Profit Allocation for Electric Vehicle Battery Data in a Data Market (Proposals Track)
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Abstract: This paper delves into the realm of electric vehicle (EV) battery data trading markets, focusing on data valuation and revenue allocation. In the face of fast-developing electric mobility, the safety of EV batteries becomes more and more important, driving the need for robust anomaly detection models. For newly found EV companies lacking extensive data, data markets offer a solution, facilitated by trading platforms. We shape this landscape, outline a transaction process involving data buyers, data sellers, and platforms. Our exploration extends to data valuation methodologies, encompassing the classic Shapley value and the least core algorithm. Considering the complicated mechanisms in EV battery, we unveil a deep learning framework for anomaly detection, treating EV batteries as dynamic systems. To explain data value from an economic perspective, we utilize a utility function considering the direct economic costs saved for the EV company to refine the evaluation process. Based on data value, we further propose revenue allocation schemes to allocate part of EV company's revenue to data sellers, offering diverse perspectives on fair and equitable profit distribution. A case study is conducted based on real world EV battery dataset to illustrate how the different revenue allocation schemes allocate payoffs to data sellers.

Authors: Junkang Chen (Peking University); Guannan He (Peking University)

Data Mining Transportation Time-series Analysis
ICLR 2024 SkyImageNet: Towards a large-scale sky image dataset for solar power forecasting (Proposals Track)
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Abstract: The variability of solar photovoltaic (PV) output, particularly that caused by rapidly changing cloud dynamics, challenges the reliability of renewable energy systems. Solar forecasting based on cloud observations collected by ground-level sky cameras shows promising performance in anticipating short-term solar power fluctuations. However, current deep learning methods often rely on a single dataset with limited sample diversity for training, and thus generalize poorly to new locations and different sky conditions. Moreover, the lack of a standardized dataset hinders the consistent comparison of existing solar forecasting methods. To close these gaps, we propose to build a large-scale standardized sky image dataset --- SkyImageNet --- by assembling, harmonizing, and processing suitable open-source datasets collected in various geographical locations. An accompanying python package will be developed to streamline the process of utilizing SkyImageNet in a machine learning framework. We hope that the outcomes of this project will foster the development of more robust forecasting systems, advance the comparability of short-term solar forecasting model performances, and further facilitate the transition to the next generation of sustainable energy systems.

Authors: Yuhao Nie (Massachusetts Institute of Technology); Quentin Paletta (European Space Research Institute); Sherrie Wang (MIT)

Computer Vision & Remote Sensing Earth Observation & Monitoring Meta- and Transfer Learning
ICLR 2024 An Adaptive Hydropower Management Approach for Downstream Ecosystem Preservation (Proposals Track)
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Abstract: Hydropower plants play a pivotal role in advancing clean and sustainable energy production, contributing significantly to the global transition towards renewable energy sources. However, hydropower plants are currently perceived both positively as sources of renewable energy and negatively as disruptors of ecosystems. In this work, we highlight the overlooked potential of using hydropower plant as protectors of ecosystems by using adaptive ecological discharges. To advocate for this perspective, we propose using a neural network to predict the minimum ecological discharge value at each desired time. Additionally, we present a novel framework that seamlessly integrates it into hydropower management software, taking advantage of the well-established approach of using traditional constrained optimisation algorithms. This novel approach not only protects the ecosystems from climate change but also contributes to potentially increase the electricity production.

Authors: Cecília Coelho (University of Minho); Ming Jin (Virginia Tech); M. Fernanda P. Costa (Dep. Mathematics, University of Minho); Luís L. Ferrás (University of Porto)

Ecosystems & Biodiversity Climate Science & Modeling Power & Energy Time-series Analysis
ICLR 2024 A Benchmark Dataset for Meteorological Downscaling (Proposals Track)
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Abstract: High spatial resolution in atmospheric representations is crucial across Earth science domains, but global reanalysis datasets like ERA5 often lack the detail to capture local phenomena due to their coarse resolution. Recent efforts have leveraged deep neural networks from computer vision to enhance the spatial resolution of meteorological data, showing promise for statistical downscaling. However, methodological diversity and insufficient comparisons with traditional downscaling techniques challenge these advancements. Our study introduces a benchmark dataset for statistical downscaling, utilizing ERA5 and the finer-resolution COSMO-REA6, to facilitate direct comparisons of downscaling methods for 2m temperature, global (solar) irradiance and 100m wind fields. Accompanying U-Net, GAN, and transformer models with a suite of evaluation metrics aim to standardize assessments and promote transparency and confidence in applying deep learning to meteorological downscaling.

Authors: Michael Langguth (Juelich Supercomputing Centre - Forschungszentrum Juelich); Paula Harder (Mila); Irene Schicker (Geos); Ankit Patnala (Juelich Supercomputing Centre - Forschungszentrum Juelich); Sebastian Lehner (GeoSphere Austria); Konrad Mayer (GeoSphere Austria); Markus Dabernig (GeoSphere Austria)

Climate Science & Modeling Earth Observation & Monitoring Power & Energy Generative Modeling
ICLR 2024 Enabling Clean Energy Resilience with Machine Learning-Empowered Underground Hydrogen Storage (Proposals Track)
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Abstract: To address the urgent challenge of climate change, there is a critical need to transition away from fossil fuels towards sustainable energy systems, with renewable energy sources playing a pivotal role. However, the inherent variability of renewable energy, without effective storage solutions, often leads to imbalances between energy supply and demand. Underground Hydrogen Storage (UHS) emerges as a promising long-term storage solution to bridge this gap, yet its widespread implementation is impeded by the high computational costs associated with high fidelity UHS simulations. This paper introduces UHS from a data-driven perspective and outlines a roadmap for integrating machine learning into UHS, thereby facilitating the large-scale deployment of UHS.

Authors: Alvaro R Carbonero Gonzales (Los Alamos National Lab); Shaowen Mao (Los Alamos National Laboratory); Mohamed Mehana (Los Alamos National Lab)

Carbon Capture & Sequestration Computer Vision & Remote Sensing
ICLR 2024 Adjustment of ocean carbon sink predictions with an emission-driven Earth system model using a deep neural network (Proposals Track)
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Abstract: Near-term predictions of the Global Carbon Budget (GCB) with Earth system models (ESMs) driven by specified CO2 emissions were used to inform the GCB annual update for the first time in 2023. These predictions are biased because they are initialized indirectly from the ESMs response to physical observational constraints, and because the ESMs themselves are imperfect representations of the climate system. We propose a deep learning-based post-processing method to adjust GCB predictions using an autoencoder, which outperforms standard bias and trend correction methods.

Authors: Reinel Sospedra-Alfonso (Environment and Climate Change Canada); Parsa Gooya (Environment and Climate Change Canada); Johannes Exenberger (Graz University of Technology)

Climate Science & Modeling
ICLR 2024 Calibrating Earth System Models with Bayesian Optimal Experimental Design (Proposals Track)
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Abstract: Earth system models (ESMs) are complex climate simulations that are critical for projecting future climate change and its impacts. However, running ESMs is extremely computationally expensive, limiting the number of simulations that can be performed. This results in significant uncertainty in key climate metrics estimated from ESM ensembles. We propose a Bayesian optimal experimental design (BOED) approach to efficiently calibrate ESM simulations to observational data by actively selecting the most informative input parameters. BOED optimises the expected information gain (EIG) to select the ESM input parameter to reduce the final uncertainty estimates in the climate metrics of interest. Initial results on a synthetic benchmark demonstrate our approach can more efficiently reduce uncertainty compared to common sampling schemes like Latin hypercube sampling.

Authors: Tim Reichelt (University of Oxford); Shahine Bouabid (University of Oxford); Luke Ong (University of Oxford); Duncan Watson-Parris (University of California San Diego); Tom Rainforth (University of Oxford)

Active Learning Climate Science & Modeling Causal & Bayesian Methods
ICLR 2024 Severe Wind Event Prediction with Multivariate Physics-Informed Deep Learning (Proposals Track)
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Abstract: Wind turbines play a crucial role in combating climate change by harnessing the force of the wind to generate clean and renewable energy. One key factor in ensuring the long-term effectiveness of wind turbines is the reduction of operating costs due to maintenance. Severe weather events, such as extreme changes in wind, can damage turbines, resulting in costly maintenance and economic losses in power production. We propose a preliminary physics-informed deep learning model to improve predictions of severe wind events and a multivariate time series extension for this work.

Authors: Willa Potosnak (Carnegie Mellon University); Cristian I Challu (Carnegie Mellon University); Kin G. Olivares (Carnegie Mellon University); James K Miller (Carnegie Mellon University); Artur Dubrawski (Carnegie Mellon University)

Hybrid Physical Models Extreme Weather Power & Energy Time-series Analysis
ICLR 2024 Empowering Safe Reinforcement Learning in Power System Control with CommonPower (Tutorials Track)
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Abstract: Reinforcement learning (RL) has become a valuable tool for addressing complex decision-making problems in power system control. However, the unique intricacies of this domain necessitate the development of specialized RL algorithms. While benchmarking problems have proven effective in advancing algorithm development in various domains, existing suites do not enable a systematic study of two key challenges in power system control: ensuring adherence to physical constraints and evaluating the impact of forecast accuracy on controller performance. This tutorial introduces the sophisticated capabilities of the CommonPower toolbox, designed to address these overlooked challenges. We guide participants in composing benchmark problems within CommonPower, leveraging predefined components, and demonstrate the creation of new components. We showcase the training of a safe RL agent to solve a benchmark problem, comparing its performance against a built-in MPC baseline. Notably, CommonPower's symbolic modeling approach enables the automatic derivation of safety shields for vanilla RL algorithms. We explain the theory behind this feature in a concise introduction to the field of safe RL. Furthermore, we present CommonPower's interface for seamlessly integrating diverse forecasting strategies into the system. The workshop emphasizes the significance of safeguarding vanilla RL algorithms and encourages researchers to systematically investigate the influence of forecast uncertainties in their experiments.

Authors: Hannah Markgraf (Technical University of Munich); Michael Eichelbeck (Technical University of Munich); Matthias Althoff (Technical University of Munich)

Reinforcement Learning Buildings
ICLR 2024 Planning for Floods & Droughts: Intro to AI-Driven Hydrological Modeling (Tutorials Track)
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Abstract: This tutorial presents an AI-driven hydrological modeling approach to advance predictions of extreme hydrological events, including floods and droughts, which are of significant socioeconomic concerns. Traditionally, physics-based hydrological models have been the mainstay for simulating rainfall-runoff dynamics and forecasting streamflow. These models, while effective, are constrained by limitations in our systematic understanding and an inability to incorporate heterogeneous data. Recently, the surge in availability of multi-scale, multi-modal hydrological data has spurred the adoption of data-driven machine learning (ML) techniques. These methods have shown promising predictive performance. However, they often struggle with generalization and reliability, especially under climate change. This tutorial introduces physics-informed ML, by leveraging data and domain knowledge, to improve prediction accuracy and trustworthiness. We will delve into uncertainty quantification methods for probabilistic predictions that are vital for climate-resilient planning in managing floods and droughts. Participants will be guided through a comprehensive workflow, encompassing data analysis, model construction, and model evaluation. This tutorial is designed to elevate researchers’ understanding of hydrological systems and provide practitioners with robust, climate-resilient water management tools. These tools are instrumental in facilitating informed decision-making, crucial in the context of climate adaptation strategies. Participants will learn: ● Heterogeneous climate and hydrology data analysis ● State-of-the-art neural network models for rainfall-runoff modeling. ● ML model construction, training, validating, and testing ● Multiple ways to build a physics-informed ML model ● Uncertainty quantification in ML model predictions. All code and data will be publicly available for researchers/practitioners to build their own models.

Authors: Kshitij Tayal (Oak Ridge National Lab); Arvind Renganathan (University of Minnesota); Dan Lu (Oak Ridge National Laboratory)

Climate Science & Modeling Oceans & Marine Systems Data Mining Time-series Analysis
ICLR 2024 Understanding drivers of climate extremes using regime-specific causal graphs (Tutorials Track)
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Abstract: The climate system is intricate, involving numerous interactions among various components at multiple spatio-temporal scales. This complexity poses a significant challenge in understanding and predicting weather extremes within the Earth's climate system. However, a better understanding of the dynamics of such events is crucial due to their profound impact on ecosystems, economies, and worldwide communities. This tutorial will offer a comprehensive guide on using Regime-PCMCI (Saggioro et al., 2020), a constraint-based causal discovery technique, to uncover the causal relationships governing anomalous climate phenomena. Regime-PCMCI is designed to uncover causal relationships in time-series where transitions between regimes exist, and different causal relationships may govern each regime. In this tutorial, we will first discuss how to frame the problem of understanding climate and weather extremes using regime-specific causal discovery. We will shortly introduce constraint-based causal discovery and present the Regime-PCMCI algorithm. To enable participants to gain hands-on experience with the algorithm, we will apply Regime-PCMCI, implemented in the open-source Python package Tigramite (https://github.com/jakobrunge/tigramite), to a real-world climate science problem. Our example will focus on validating hypothesized regime-specific causal graphs that describe the causal relationship between atmospheric circulation, temperature, rainfall, evaporation, and soil moisture under various moisture regimes. Our tutorial will cover essential steps such as data preprocessing, parameter selection, and interpretation of results, ensuring that all participants with a basic understanding of climate science or data analysis can grasp the presented concepts. With this tutorial, we wish to equip participants with the skills to apply Regime-PCMCI in their research to further uncover complex mechanisms in climate science, as this knowledge is crucial for more informed policy-making.

Authors: Oana-Iuliana Popescu (Institute of Data Science, German Aerospace Center (DLR)); Wiebke Günther (German Aerospace Center); Raed Hamed (Institute for Environmental Studies, VU Amsterdam); Dominik Schumacher (4Institute for Atmospheric and Climate Science, ETH Zürich); Martin Rabel (DLR); Dim Coumou (IVM/VU); Jakob Runge (Institute of Data Science, German Aerospace Center (DLR))

Causal & Bayesian Methods Climate Science & Modeling Time-series Analysis
ICLR 2024 One Prompt Fits All: Visual Prompt-Tuning for Remote Sensing Segmentation (Tutorials Track) Audience Choice
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Abstract: Image segmentation is crucial in climate change research for analyzing satellite imagery. This technique is vital for ecosystems mapping, natural disasters assessment, and urban and agricultural planning. The advent of vision-based foundational models like the Segment Anything Model (SAM) opens new avenues in climate research and remote sensing (RS). SAM can perform segmentation tasks on any object from manually-crafted prompts. However, the efficacy of SAM largely depends on the quality of these prompts. This issue is particularly pronounced with RS data, which are inherently complex. To use SAM for accurate segmentation at scale for RS, one would need to create complex prompts for each image, which typically involves selecting dozens of points. To address this, we introduce Prompt-Tuned SAM (PT-SAM), a method that minimizes the need for manual input through a trainable, lightweight prompt embedding. This embedding captures key semantic information for specific objects of interest that would be applicable to unseen images. Our approach merges the zero-shot generalization capabilities of the pre-trained SAM model with supervised learning. Importantly, the training process for the prompt embedding not only has minimal hardware requirements, allowing it to be conducted on a CPU, but it also requires only a small dataset. With PT-SAM, image segmentation on RS data can be performed at scale without human intervention, achieving accuracies comparable to those of human-designed prompts with SAM. For example, PT-SAM can be used for analyzing forest cover across vast areas, a key factor in understanding the impact of human activities on forests. Its capability to segment a multitude of images makes it ideal for monitoring widespread land-cover changes, providing deeper insights into urbanization. This tutorial will explore how to train and utilize PT-SAM for large-scale segmentation tasks, specifically focusing on training embeddings that capture forests, and buildings.

Authors: Marshall Wang (Vector Institute); John Willes (Vector Institute); Deval Pandya (Vector Institute)

Computer Vision & Remote Sensing Earth Observation & Monitoring Forests
ICLR 2024 1. Building Sustainable Futures: Tutorial on Carbon Footprint Analysis and Mitigation Strategies Using Counter Factual Queries (Tutorials Track)
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Abstract: As the sense of urgency regarding climate change continues to mount with growing regulatory pressure across the globe, it has become increasingly crucial for enterprises and governments to align their goals with sustainability values. They face a crucial imperative to act on climate change mitigation by disclosing their GHG emissions and committing to reduction and optimization of emissions from their industrial activities including operations, infrastructure, logistics, and supply chains. The world's largest enterprises have set long-term net-zero targets but lacks an integrated view of how their key business operations and processes contribute to their sustainability journey, which makes it difficult for them to embark on a well-planned journey to achieve their sustainability goals. With the recent advancement, AI intervention becomes imperative to measure, track, and improve ESG performance to achieve sustainability goals. This tutorial aims to provide a comprehensive guide on leveraging advanced AI techniques for analysing and mitigating carbon footprints in various sectors. The tutorial covers the utilization of a generalized framework that integrates sector-specific and cross-sector enterprise data, including assets and operations, to derive actionable insights. The framework also uses additional data such as weather parameters and contextual information to facilitate a holistic approach to carbon footprint analysis and its mitigation strategies. The tutorial will delve into the working of a framework which comprises of an LLM driven carbon accounting engine, predictive models for carbon emissions, anomaly detection models, and counterfactual models. It identifies the emission hotspots, thereafter provides actionable recommendations to mitigate the carbon emission. The proposed tutorial aims to empower participants with the knowledge and skills to make informed decisions towards building a more sustainable future

Authors: Kumar Saurav (IBM); Manikandan Padmanaban (IBM Research India); Ayush Jain (IBM Research); Jagabondhu Hazra (IBM Research India)

Climate Science & Modeling Recommender Systems
NeurIPS 2023 Machine learning for gap-filling in greenhouse gas emissions databases (Papers Track)
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Abstract: Greenhouse Gas (GHG) emissions datasets are often incomplete due to inconsistent reporting and poor transparency. Filling the gaps in these datasets allows for more accurate targeting of strategies to accelerate the reduction of GHG emissions. This study evaluates the potential of machine learning methods to automate the completion of GHG datasets. We use 3 datasets of increasing complexity with 18 different gap-filling methods and provide a guide to which methods are useful in which circumstances. If few dataset features are available, or the gap consists only of a missing time step in a record, then simple interpolation is often the most accurate method and complex models should be avoided. However, if more features are available and the gap involves non-reporting emitters, then machine learning methods can be more accurate than simple extrapolation. Furthermore, the secondary output of feature importance from complex models allows for data collection prioritisation to accelerate the improvement of datasets. Graph based methods are particularly scalable due to the ease of updating predictions given new data and incorporating multimodal data sources. This study can serve as a guide to the community upon which to base ever more integrated frameworks for automated detailed GHG emissions estimations, and implementation guidance is available at https://hackmd.io/@luke-scot/ML-for-GHG-database-completion.

Authors: Luke Cullen (University of Cambridge); Andrea Marinoni (UiT the Arctic University of Norway); Jonathan M Cullen (University of Cambridge)

Public Policy
NeurIPS 2023 EarthPT: a foundation model for Earth Observation (Papers Track)
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Abstract: We introduce EarthPT -- an Earth Observation (EO) pretrained transformer. EarthPT is a 700 million parameter decoding transformer foundation model trained in an autoregressive self-supervised manner and developed specifically with EO use-cases in mind. We demonstrate that EarthPT is an effective forecaster that can accurately predict future pixel-level surface reflectances across the 400-2300 nm range well into the future. For example, forecasts of the evolution of the Normalised Difference Vegetation Index (NDVI) have a typical error of approximately 0.05 (over a natural range of -1 -> 1) at the pixel level over a five month test set horizon, out-performing simple phase-folded models based on historical averaging. We also demonstrate that embeddings learnt by EarthPT hold semantically meaningful information and could be exploited for downstream tasks such as highly granular, dynamic land use classification. Excitingly, we note that the abundance of EO data provides us with -- in theory -- quadrillions of training tokens. Therefore, if we assume that EarthPT follows neural scaling laws akin to those derived for Large Language Models (LLMs), there is currently no data-imposed limit to scaling EarthPT and other similar ‘Large Observation Models.’

Authors: Michael J Smith (Aspia Space); Luke Fleming (Aspia Space); James Geach (Aspia Space)

Earth Observation & Monitoring
NeurIPS 2023 Towards Understanding Climate Change Perceptions: A Social Media Dataset (Papers Track)
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Abstract: Climate perceptions shared on social media are an invaluable barometer of public attention. By directing research towards this topic, we can eventually improve the effectiveness of climate change communication, increase public engagement, and enhance climate change education. We propose two real-world image datasets to promote impactful research both in the Computer Vision community and beyond. Firstly, ClimateTV, a dataset containing over 700,000 climate change-related images posted on Twitter and labelled on basis of the image hashtags. Secondly, ClimateCT, a Twitter dataset containing images with five-dimensional annotations in super-categories (i) Animals, (ii) Climate action, (iii) Consequences, (iv) Setting, and (v) Type. These challenging classification datasets contain classes which are designed according to their relevance in the context of climate change. The challenging nature of the datasets is given by varying class diversities (e.g. polar bear vs. land mammal) and foci (e.g. arctic vs. snowy residential area). The analyses of our datasets using CLIP embeddings and query optimization (CoCoOp) further showcase the challenging nature of ClimateTV and ClimateCT.

Authors: Katharina Prasse (University of Siegen); Steffen Jung (MPII); Isaac B Bravo (Technische Universität München); Stefanie Walter (Technical University of Munich); Margret Keuper (University of Siegen, Max Planck Institute for Informatics)

Behavioral and Social Science
NeurIPS 2023 Adaptive-Labeling for Enhancing Remote Sensing Cloud Understanding (Papers Track)
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Abstract: Cloud analysis is a critical component of weather and climate science, impacting various sectors like disaster management. However, achieving fine-grained cloud analysis, such as cloud segmentation, in remote sensing remains challenging due to the inherent difficulties in obtaining accurate labels, leading to significant labeling errors in training data. Existing methods often assume the availability of reliable segmentation annotations, limiting their overall performance. To address this inherent limitation, we introduce an innovative model-agnostic Cloud Adaptive-Labeling (CAL) approach, which operates iteratively to enhance the quality of training data annotations and consequently improve the performance of the learned model. Our methodology commences by training a cloud segmentation model using the original annotations. Subsequently, it introduces a trainable pixel intensity threshold for adaptively labeling the cloud training images on-the-fly. The newly generated labels are then employed to fine-tune the model. Extensive experiments conducted on multiple standard cloud segmentation benchmarks demonstrate the effectiveness of our approach in significantly boosting the performance of existing segmentation models. Our CAL method establishes new state-of-the-art results when compared to a wide array of existing alternatives.

Authors: Jay Gala (NMIMS); Sauradip Nag (University of Surrey); Huichou Huang (City University of Hong Kong); Ruirui Liu (Brunel University London); Xiatian Zhu (University of Surrey)

Climate Science & Modeling
NeurIPS 2023 Flamingo: Environmental Impact Factor Matching for Life Cycle Assessment with Zero-Shot ML (Papers Track)
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Abstract: Consumer products contribute to >75% of global greenhouse gas (GHG) emissions, primarily through indirect contributions from the supply chain. Measurement of GHG emissions associated with products is crucial to quantify the impact of GHG emission abatement actions. Life cycle assessment (LCA), the scientific discipline for measuring GHG emissions, estimates the environmental impact of a product. Scaling LCA to millions of products is challenging as it requires extensive manual analysis by domain experts. To avoid repetitive analysis, environmental impact factors (EIF) of common materials and products are published for use by experts. However, finding appropriate EIFs for even a single product can require hundreds of hours of manual work, especially for complex products. We present Flamingo, an algorithm that leverages neural language models to automatically identify an appropriate EIF given a text description. A key challenge in automation is that EIF databases are incomplete. Flamingo uses industry sector classification as an intermediate layer to identify when there are no good matches in the database. On a dataset of 664 products, Flamingo achieves an EIF matching precision of 75%.

Authors: Bharathan Balaji (Amazon); Venkata Sai Gargeya Vunnava (amazon); Nina Domingo (Amazon); Shikhar Gupta (Amazon); Harsh Gupta (Amazon); Geoffrey Guest (Amazon); Aravind Srinivasan (Amazon); Kellen Axten (Amazon); Jared Kramer (Amazon)

Natural Language Processing
NeurIPS 2023 AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning (Papers Track)
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Abstract: AtmoRep is a novel, task-independent stochastic computer model of atmospheric dynamics inspired by the concept of foundation models in natural language processing, like the GPT line or PalmX, applied in the context of Earth system science. The main innovative aspect consists in the fact that the model can skillfully solve scientific tasks it was not specifically trained on, clearly exhibiting in-context learning capabilities. AtmoRep's skill has been tested on nowcasting, temporal interpolation, model correction, and counterfactuals, demonstrating that large-scale neural networks can provide skillful, task-independent models able to complement the existing numerical approaches in multiple applications. In addition, the authors also demonstrated the possibility to further increase the model accuracy by fine tuning it directly on observational data for tasks such as precipitation corrections or downscaling.

Authors: ilaria luise (CERN)

Climate Science & Modeling
NeurIPS 2023 Artificial Intelligence for Methane Mitigation : Through an Automated Determination of Oil and Gas Methane Emissions Profiles (Papers Track)
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Abstract: The oil and gas sector is the second largest anthropogenic emitter of methane, which is responsible for approximately 25% of global warming since pre-industrial times. In order to mitigate methane atmospheric emissions from oil and gas industry, the potential emitting infrastructure must be monitored. Initiatives such as the Methane Alert and Response System (MARS), launched by the United Nations Environment Program, aim to locate significant emissions events, alert relevant stakeholders, as well as monitor and track progress in mitigation efforts. To achieve this goal, an automated solution is needed for consistent monitoring across multiple oil and gas basins around the world. Most methane emissions analysis studies propose post-emission analysis. The works and future guidelines presented in this paper aim to provide an automated collection of informed methane emissions by oil and gas site and infrastructure which are necessary to dress emission profile in near real time. This proposed framework also permits to create action margins to reduce methane emissions by passing from post methane emissions analysis to forecasting methods.

Authors: Jade Eva Guisiano (Sorbonne / ISEP / Polytechnique / UNEP); Thomas LAUVAUX (Université de Reims); Eric Moulines (Ecole Polytechnique); Jérémie Sublime (ISEP)

Earth Observation & Monitoring
NeurIPS 2023 Augmenting Ground-Level PM2.5 Prediction via Kriging-Based Pseudo-Label Generation (Papers Track)
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Abstract: Fusing abundant satellite data with sparse ground measurements constitutes a major challenge in climate modeling. To address this, we propose a strategy to augment the training dataset by introducing unlabeled satellite images paired with pseudo-labels generated through a spatial interpolation technique known as ordinary kriging, thereby making full use of the available satellite data resources. We show that the proposed data augmentation strategy helps enhance the performance of the state-of-the-art convolutional neural network-random forest (CNN-RF) model by a reasonable amount, resulting in a noteworthy improvement in spatial correlation and a reduction in prediction error.

Authors: lei Duan (Duke University); Ziyang Jiang (Duke University); David Carlson (Duke University)

Computer Vision & Remote Sensing
NeurIPS 2023 Physics-Informed Domain-Aware Atmospheric Radiative Transfer Emulator for All Sky Conditions (Papers Track)
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Abstract: Radiative transfer modeling is a complex and computationally expensive process that is used to predict how radiation interacts with the atmosphere and Earth's surface. The Rapid Radiation Transfer Model (RRTM) is one such process model that is used in many Earth system models. In recent years, there has been a growing interest in using machine learning (ML) to speed up radiative transfer modeling. ML algorithms can be trained on large datasets of existing RRTM simulations to learn how to predict the results of new simulations without having to run the full RRTM model so one can use the algorithm for new simulations with very light computational demand. This study developed a new physics-based ML emulator for RRTM that is built on a convolutional neural network (CNN) where we trained the CNN on a dataset of 28 years of RRTM simulations. We built a custom loss function, which incorporates information on how radiation interacts with clouds at day- and night-time. The emulator was able to learn how to predict the vertical heating rates in the atmosphere with a high degree of accuracy (RMSE of less than 2% and Pearson's correlation above 0.9). The new ML emulator is over 56 times faster than the original RRTM model on traditional multi-CPU machines. This speedup could allow scientists to call the RRTM much more frequently in atmosphere models, which may improve the accuracy of climate models and reduce the uncertainty in the future climate projections.

Authors: Piyush Garg (Argonne National Laboratory); Emil Constantinescu (Argonne National Laboratory); Bethany Lusch (Argonne National Lab); Troy Arcomano (Argonne National Laboratory); Jiali Wang (Argonne National Laboratory); Rao Kotamarthi (Argonne National Laboratory)

Climate Science & Modeling Hybrid Physical Models
NeurIPS 2023 Can We Reliably Improve the Robustness to Image Acquisition of Remote Sensing of PV Systems? (Papers Track)
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Abstract: Photovoltaic (PV) energy is crucial for the decarbonization of energy systems. Due to the lack of centralized data, remote sensing of rooftop PV installations is the best option to monitor the evolution of the rooftop PV installed fleet at a regional scale. However, current techniques lack reliability and are notably sensitive to shifts in the acquisition conditions. To overcome this, we leverage the wavelet scale attribution method (WCAM), which decomposes a model's prediction in the space-scale domain. The WCAM enables us to assess on which scales the representation of a PV model rests and provides insights to derive methods that improve the robustness to acquisition conditions, thus increasing trust in deep learning systems to encourage their use for the safe integration of clean energy in electric systems.

Authors: Gabriel Kasmi (Mines Paris - PSL); Laurent Dubus (RTE France); Yves-Marie Saint-Drenan (Mines Paris - PSL); Philippe Blanc (Mines Paris - PSL)

Computer Vision & Remote Sensing
NeurIPS 2023 Uncertainty Quantification of the Madden–Julian Oscillation with Gaussian Processes (Papers Track)
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Abstract: The Madden–Julian Oscillation (MJO) is an influential climate phenomenon that plays a vital role in modulating global weather patterns. In spite of the improvement in MJO predictions made by machine learning algorithms, such as neural networks, most of them cannot provide the uncertainty levels in the MJO forecasts directly. To address this problem, we develop a nonparametric strategy based on Gaussian process (GP) models. We calibrate GPs using empirical correlations. Furthermore, we propose a posteriori covariance correction that extends the probabilistic coverage by more than three weeks.

Authors: Haoyuan Chen (Texas A&M University); Emil Constantinescu (Argonne National Laboratory); Vishwas Rao (Argonne National Laboratory); Cristiana Stan (George Mason University)

Climate Science & Modeling Uncertainty Quantification & Robustness
NeurIPS 2023 Ocean Wave Energy: Optimizing Reinforcement Learning Agents for Effective Deployment (Papers Track)
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Abstract: Fossil fuel energy production is a leading cause of climate change. While wind and solar energy have made advancements, ocean waves, a more consistent clean energy source, remain underutilized. Wave Energy Converters (WEC) transform wave power into electric energy. To be economically viable, modern WECs need sophisticated real-time controllers that boost energy output and minimize mechanical stress, thus lowering the overall cost of energy (LCOE). This paper presents how a Reinforcement Learning (RL) controller can outperform the default spring damper controller for complex spread waves in the sea, enhancing wave energy's viability. Using the Proximal Policy Optimization (PPO) algorithm with Transformer variants as function approximators, the RL controllers optimize multi-generator Wave Energy Converters (WEC), leveraging wave sensor data for multiple cost-efficiency goals. After successful tests in the EuropeWave\footnote{EuropeWave: https://www.europewave.eu/} project's emulator tank, the platform is planned to deploy. We discuss the challenges of deployment at the BiMEP site and how we had to tune the RL controller to address that. The RL controller outperforms the default Spring Damper controller in the BiMEP\footnote{BiMEP: https://www.bimep.com/en/} conditions by 22.8% on energy capture. Enhancing wave energy's economic viability will expedite the transition to clean energy, reducing carbon emissions and fostering a healthier climate.

Authors: Vineet Gundecha (Hewlett Packard Enterpise); Sahand Ghorbanpour (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Avisek Naug (Hewlett Packard Enterprise); Alexandre Pichard (Carnegie Clean Energy); mathieu Cocho (Carnegie Clean Energy); Soumyendu Sarkar (Hewlett Packard Enterprise)

Power & Energy Reinforcement Learning
NeurIPS 2023 Sustainable Data Center Modeling: A Multi-Agent Reinforcement Learning Benchmark (Papers Track)
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Abstract: The rapid growth of machine learning (ML) has led to an increased demand for computational power, resulting in larger data centers (DCs) and higher energy consumption. To address this issue and reduce carbon emissions, intelligent control of DC components such as cooling, load shifting, and energy storage is essential. However, the complexity of managing these controls in tandem with external factors like weather and green energy availability presents a significant challenge. While some individual components like HVAC control have seen research in Reinforcement Learning (RL), there's a gap in holistic optimization covering all elements simultaneously. To tackle this, we've developed DCRL, a multi-agent RL environment that empowers the ML community to research, develop, and refine RL controllers for carbon footprint reduction in DCs. DCRL is a flexible, modular, scalable, and configurable platform that can handle large High Performance Computing (HPC) clusters. In its default setup, DCRL also provides a benchmark for evaluating multi-agent RL algorithms, facilitating collaboration and progress in green computing research.

Authors: Soumyendu Sarkar (Hewlett Packard Enterprise); Avisek Naug (Hewlett Packard Enterprise); Antonio Guillen (Hewlett Packard Enterprise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Sahand Ghorbanpour (Hewlett Packard Enterprise); Sajad Mousavi (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs)

Buildings Reinforcement Learning
NeurIPS 2023 ACE: A fast, skillful learned global atmospheric model for climate prediction (Papers Track)
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Abstract: Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model. The formulation of ACE allows evaluation of physical laws such as the conservation of mass and moisture. The emulator is stable for 100 years, nearly conserves column moisture without explicit constraints and faithfully reproduces the reference model's climate, outperforming a challenging baseline on over 90% of tracked variables. ACE requires nearly 100x less wall clock time and is 100x more energy efficient than the reference model using typically available resources. Without fine-tuning, ACE can stably generalize to a previously unseen historical sea surface temperature dataset.

Authors: Oliver Watt-Meyer (Allen Institute for AI); Gideon Dresdner (Allen Institute for AI Climate Science); Jeremy McGibbon (Allen Institute for AI); Spencer K Clark (Allen Institute for Artificial Intelligence); James Duncan (University of California, Berkeley); Brian Henn (Allen Institute for AI); Matthew Peters (AI2); Noah D Brenowitz (NVIDIA); Karthik Kashinath (NVIDIA); Mike Pritchard (NVIDIA); Boris Bonev (NVIDIA); Christopher Bretherton (Allen Institute for AI)

Climate Science & Modeling
NeurIPS 2023 A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration (Papers Track)
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Abstract: There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers. The design and intelligent control for data centers have an important impact on data center carbon footprint. In this paper, we showcase PyDCM, a Python library that enables extremely fast prototyping of data center design and applies reinforcement learning-enabled control with the purpose of evaluating key sustainability metrics, including carbon footprint, energy consumption, and observing temperature hotspots. We demonstrate these capabilities of PyDCM and compare them to existing works in EnergyPlus for modeling data centers. PyDCM can also be used as a standalone Gymnasium environment for demonstrating sustainability-focused data center control.

Authors: Avisek Naug (Hewlett Packard Enterprise); Antonio Guillen (Hewlett Packard Enterprise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Sahand Ghorbanpour (Hewlett Packard Enterprise); Sajad Mousavi (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Soumyendu Sarkar (Hewlett Packard Enterprise)

Buildings Reinforcement Learning
NeurIPS 2023 Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean (Papers Track)
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Abstract: Comprehensive information on housing stock is crucial for climate adaptation initiatives aiming to reduce the adverse impacts of climate-extreme hazards in high-risk regions like the Caribbean. In this study, we propose a workflow for rapidly generating critical baseline housing stock data using very high-resolution drone images and deep learning techniques. Specifically, our work leverages the Segment Anything Model and convolutional neural networks for the automated generation of building footprint and roof classification maps. By enhancing local capacity in government agencies, this work seeks to improve the climate resilience of the housing sector in small island developing states in the Caribbean.

Authors: Isabelle Tingzon (The World Bank); Nuala Margaret Cowan (The World Bank); Pierre Chrzanowski (The World Bank)

Disaster Management and Relief
NeurIPS 2023 Graph-based Neural Weather Prediction for Limited Area Modeling (Papers Track)
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Abstract: The rise of accurate machine learning methods for weather forecasting is creating radical new possibilities for modeling the atmosphere. In the time of climate change, having access to high-resolution forecasts from models like these is also becoming increasingly vital. While most existing Neural Weather Prediction (NeurWP) methods focus on global forecasting, an important question is how these techniques can be applied to limited area modeling. In this work we adapt the graph-based NeurWP approach to the limited area setting and propose a multi-scale hierarchical model extension. Our approach is validated by experiments with a local model for the Nordic region.

Authors: Joel Oskarsson (Linköping University); Tomas Landelius (SMHI); Fredrik Lindsten (Linköping University)

Climate Science & Modeling Time-series Analysis
NeurIPS 2023 Self-supervised Pre-training for Precipitation Post-processor (Papers Track)
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Abstract: Securing sufficient forecast lead time for local precipitation is essential for preventing hazardous weather events. Nonetheless, global warming-induced climate change is adding to the challenge of accurately predicting severe precipitation events, such as heavy rainfall. In this work, we propose a deep learning-based precipitation post-processor approach to numerical weather prediction (NWP) models. The precipitation post-processor consists of (i) self-supervised pre-training, where parameters of encoder are pre-trained on the reconstruction of masked variables of the atmospheric physics domain, and (ii) transfer learning on precipitation segmentation tasks (target domain) from the pre-trained encoder. We also introduce a heuristic labeling approach for effectively training class-imbalanced datasets. Our experiment results in precipitation correction for regional NWP show that the proposed method outperforms other approaches.

Authors: Sojung An (Korea Institute of Atmosphere Prediction Systems); Junha Lee (Korea Institute of Industrial Technology); Jiyeon Jang (Korea Institute of Atmosphere Prediction Systems); Inchae Na (Korea Institute of Atmosphere Prediction Systems); Wooyeon Park (Korea Institute of Atmosphere Prediction Systems); Sujeong You (KITECH)

Extreme Weather
NeurIPS 2023 Enhancing Data Center Sustainability with a 3D CNN-Based CFD Surrogate Model (Papers Track)
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Abstract: Thermal Computational Fluid Dynamics (CFD) models analyze airflow and heat distribution in data centers, but their complex computations hinder efficient energy-saving optimizations for sustainability. We introduce a new method to acquire data and model 3D Convolutional Neural Network (CNN) based surrogates for CFDs, which predict a data center's temperature distribution based on server workload, HVAC airflow rate, and temperature set points. The surrogate model's predictions are highly accurate, with a mean absolute error of 0.31°C compared to CFD-based ground truth temperatures. The surrogate model is three orders of magnitude faster than CFDs in generating the temperature maps for similar-sized data centers, enabling real-time applications. It helps to quickly identify and reduce temperature hot spots($7.7%) by redistributing workloads and saving cooling energy($2.5%). It also aids in optimizing server placement during installation, preventing issues, and increasing equipment lifespan. These optimizations boost sustainability by reducing energy use, improving server performance, and lowering environmental impact.

Authors: Soumyendu Sarkar (Hewlett Packard Enterprise); Antonio Guillen (Hewlett Packard Enterprise); Zachariah Carmichael (University of Notre Dame); Vineet Gundecha (Hewlett Packard Enterpise); Avisek Naug (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Ricardo Luna Gutierrez (Hewlett Packard Enterprise)

Buildings Computer Vision & Remote Sensing
NeurIPS 2023 Price-Aware Deep Learning for Electricity Markets (Papers Track)
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Abstract: While deep learning gradually penetrates operational planning of power systems, its inherent prediction errors may significantly affect electricity prices. This paper examines how prediction errors propagate into electricity prices, revealing notable pricing errors and their spatial disparity in congested power systems. To improve fairness, we propose to embed electricity market-clearing optimization as a deep learning layer. Differentiating through this layer allows for balancing between prediction and pricing errors, as oppose to minimizing prediction errors alone. This layer implicitly optimizes fairness and controls the spatial distribution of price errors across the system. We showcase the price-aware deep learning in the nexus of wind power forecasting and short-term electricity market clearing.

Authors: Vladimir Dvorkin (Massachusetts Institute of Technology); Ferdinando Fioretto (University of Virginia)

Power & Energy
NeurIPS 2023 A machine learning framework for correcting under-resolved simulations of turbulent systems using nudged datasets (Papers Track)
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Abstract: Due to the rapidly changing climate, the frequency and severity of extreme weather, such as storms and heatwaves is expected to increase drastically over the coming decades. Accurately quantifying the risk of such events with high spatial resolution is a critical step in the implementation of strategies to prepare for and mitigate the damages. As fully resolved simulations remain computationally out of reach, policy makers must rely on coarse resolution climate models which either parameterize or completely ignore sub-grid scale dynamics. In this work we propose a machine learning framework to debias under-resolved simulations of complex and chaotic dynamical systems such as atmospheric dynamics. The proposed strategy uses ``nudged'' simulations of the coarse model to generate training data designed to minimize the effects of chaotic divergence. We illustrate through a prototype QG model that the proposed approach allows us to machine learn a map from the chaotic attractor of under-resolved dynamics to that of the fully resolved system. In this way we are able to recover extreme event statistics using a very small training dataset.

Authors: Benedikt Barthel (MIT); Themis Sapsis (MIT)

Climate Science & Modeling
NeurIPS 2023 Can Deep Learning help to forecast deforestation in the Amazonian Rainforest? (Papers Track)
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Abstract: Deforestation is a major driver of climate change. To mitigate deforestation, carbon offset projects aim to protect forest areas at risk. However, existing literature shows that most projects have substantially overestimated the risk of deforestation, thereby issuing carbon credits without equivalent emissions reductions. In this study, we examine if the spread of deforestation can be predicted ex-ante using Deep Learning (DL) models. Our input data includes past deforestation development, slope information, land use, and other terrain- and soil-specific covariates. Testing predictions 1-year ahead, we find that our models only achieve low levels of predictability. For pixel-wise classification at a 30 m resolution, our models achieve an F1 score of 0.263. Only when substantially simplifying the task to predicting if any level of deforestation occurs within a 1.5 km squared tile, the model results improve to a moderate performance (F1: 0.608). We conclude that, based on our input data, deforestation cannot be predicted accurately enough to justify the ex-ante issuance of carbon credits for forest conservation projects. As main challenges, there is the extreme class imbalance between pixels that are deforested (minority) and not deforested (majority) as well as the omittance of social, political, and economic drivers of deforestation.

Authors: Tim Engelmann (ETH Zurich); Malte Toetzke (ETH Zurich)

Forests Time-series Analysis
NeurIPS 2023 Spatially-resolved emulation of climate extremes via machine learning stochastic models (Papers Track)
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Abstract: Emulators, or reduced-complexity models, serve as an ideal complement to earth system models (ESM) by providing the climate information under various scenarios at much lower computational costs. We develop an emulator of climate extremes that produce the temporal evolution of probability distributions of local variables on a spatially resolved grid. The representative modes of climate change are identified using principal component analysis (PCA), and the PCA time series are approximated using stochastic models. When applied to ERA5 data, the model accurately reproduces the quantiles of local daily maximum temperature and effectively captures the non-Gaussian statistics. We also discuss potential generalization of our emulator to different climate change scenarios.

Authors: Mengze Wang (Massachusetts Institute of Technology); Andre Souza (Massachusetts Institute of Technology); Raffaele Ferrari (Massachusetts Institute of Technology); Themis Sapsis (MIT)

Climate Science & Modeling
NeurIPS 2023 Reinforcement Learning control for Airborne Wind Energy production (Papers Track)
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Abstract: Airborne Wind Energy (AWE) is an emerging technology that promises to be able to harvest energy from strong high-altitude winds, while addressing some of the key critical issues of current wind turbines. AWE is based on flying devices (usually gliders or kites) that, tethered to a ground station, fly driven by the wind and convert the mechanical energy of wind into electrical energy by means of a generator. Such systems are usually controlled by adjusting the trajectory of the kite using optimal control techniques, such as model-predictive control. These methods are based upon a mathematical model of the system to control, and they produce results that are strongly dependent on the specific model at use and difficult to generalize. Our aim is to replace these classical techniques with an approach based on Reinforcement Learning (RL), which can be used even in absence of a known model. Experimental results prove that RL is a viable method to control AWE systems in complex simulated environments, including turbulent flows.

Authors: Lorenzo Basile (University of Trieste); Maria Grazia Berni (University of Trieste); Antonio Celani (ICTP)

Power & Energy Reinforcement Learning
NeurIPS 2023 Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence (Papers Track)
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Abstract: Global climate models (GCMs) are the main tools for understanding and predicting climate change. However, due to limited numerical resolutions, these models suffer from major structural uncertainties; e.g., they cannot resolve critical processes such as small-scale eddies in atmospheric and oceanic turbulence. Thus, such small-scale processes have to be represented as a function of the resolved scales via closures (parametrization). The accuracy of these closures is particularly important for capturing climate extremes. Traditionally, such closures are based on heuristics and simplifying assumptions about the unresolved physics. Recently, supervised-learned closures, trained offline on high-fidelity data, have been shown to outperform the classical physics-based closures. However, this approach requires a significant amount of high-fidelity training data and can also lead to instabilities. Reinforcement learning is emerging as a potent alternative for developing such closures as it requires only low-order statistics and leads to stable closures. In Scientific Multi-Agent Reinforcement Learning (SMARL) computational elements serve a dual role of discretization points and learning agents. Here, we leverage SMARL and fundamentals of turbulence physics to learn closures for canonical prototypes of atmospheric and oceanic turbulence. The policy is trained using only the enstrophy spectrum, which is nearly invariant and can be estimated from a few high-fidelity samples. We show that these closures lead to stable low-resolution simulations that, at a fraction of the cost, can reproduce the high-fidelity simulations' statistics, including the tails of the probability density functions (PDFs). These results demonstrate the high potential of SMARL for closure modeling for GCMs, especially in the regime of scarce data and indirect observations.

Authors: Rambod Mojgani (Rice University); Daniel Waelchli (ETHZ); Yifei Guan (Rice University); Petros Koumoutsakos (Harvard); Pedram Hassanzadeh (Rice University)

Climate Science & Modeling Hybrid Physical Models
NeurIPS 2023 Machine learning derived sub-seasonal to seasonal extremes (Papers Track)
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Abstract: Improving the accuracy of sub-seasonal to seasonal (S2S) extremes can significantly impact society. Providing S2S forecasts in risk or extreme indices can aid disaster response, especially for drought and flood events. Additionally, it can provide updates on disease outbreaks and aid in predicting the occurrence, duration, and decline of heat waves. This work uses a transformer model to predict the daily temperature distributions in the S2S scale. We analyze how the model performs in extreme temperatures by comparing its output distributions with those obtained from ECMWF forecasts across different metrics. Our model produces better responses for temperatures in average and extreme regions. Also, we show how our model better captures the heatwave that hit Europe in the summer of 2019.

Authors: Daniel Salles Civitarese (IBM Research, Brazil); Bianca Zadrozny (IBM Research)

Time-series Analysis Extreme Weather
NeurIPS 2023 Understanding Opinions Towards Climate Change on Social Media (Papers Track)
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Abstract: Social media platforms such as Twitter (now known as X) have revolutionized how the public engage with important societal and political topics. Recently, climate change discussions on social media became a catalyst for political polarization and the spreading of misinformation. In this work, we aim to understand how real world events influence the opinions of individuals towards climate change related topics on social media. To this end, we extracted and analyzed a dataset of 13.6 millions tweets sent by 3.6 million users from 2006 to 2019. Then, we construct a temporal graph from the user-user mentions network and utilize the Louvain community detection algorithm to analyze the changes in community structure around Conference of the Parties on Climate Change (COP) events. Next, we also apply tools from the Natural Language Processing literature to perform sentiment analysis and topic modeling on the tweets. Our work acts as a first step towards understanding the evolution of pro-climate change communities around COP events. Answering these questions helps us understand how to raise people's awareness towards climate change thus hopefully calling on more individuals to join the collaborative effort in slowing down climate change.

Authors: Yashaswi Pupneja (University of Montreal); Yuesong Zou (McGill University); Sacha Levy (Yale University); Shenyang Huang (Mila/McGill University)

Behavioral and Social Science Data Mining
NeurIPS 2023 Real-time Carbon Footprint Minimization in Sustainable Data Centers wth Reinforcement Learning (Papers Track) Best ML Innovation
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Abstract: As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. There is a pressing need to optimize energy usage in these centers, especially considering factors like cooling, balancing flexible load based on renewable energy availability, and battery storage utilization. The challenge arises due to the interdependencies of these strategies with fluctuating external factors such as weather and grid carbon intensity. Although there's currently no real-time solution that addresses all these aspects, our proposed Data Center Carbon Footprint Reduction (DC-CFR) framework, based on multi-agent Reinforcement Learning (MARL), targets carbon footprint reduction, energy optimization, and cost. Our findings reveal that DC-CFR's MARL agents efficiently navigate these complexities, optimizing the key metrics in real-time. DC-CFR reduced carbon emissions, energy consumption, and energy costs by over 13% with EnergyPlus simulation compared to the industry standard ASHRAE controller controlling HVAC for a year in various regions.

Authors: Soumyendu Sarkar (Hewlett Packard Enterprise); Avisek Naug (Hewlett Packard Enterprise); Ricardo Luna Gutierrez (Hewlett Packard Enterprise); Antonio Guillen (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Cullen Bash (HPE)

Buildings Reinforcement Learning
NeurIPS 2023 Machine learning applications for weather and climate predictions need greater focus on extremes (Papers Track)
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Abstract: Multiple studies have now demonstrated that machine learning (ML) can give improved skill for predicting or simulating fairly typical weather events, for tasks such as short-term and seasonal weather forecasting, downscaling simulations to higher resolution and emulating and speeding up expensive model parameterisations. Many of these used ML methods with very high numbers of parameters, such as neural networks, which are the focus of the discussion here. Not much attention has been given to the performance of these methods for extreme event severities of relevance for many critical weather and climate prediction applications, with return periods of more than a few years. This leaves a lot of uncertainty about the usefulness of these methods, particularly for general purpose prediction systems that must perform reliably in extreme situations. ML models may be expected to struggle to predict extremes due to there usually being few samples of such events. However, there are some studies that do indicate that ML models can have reasonable skill for extreme weather, and that it is not hopeless to use them in situations requiring extrapolation. This paper reviews these studies, updating an earlier review, and argues that this is an area that needs researching more. Ways to get a better understanding of how well ML models perform at predicting extreme weather events are discussed.

Authors: Peter Watson (Bristol)

Climate Science & Modeling Extreme Weather
NeurIPS 2023 Climate-sensitive Urban Planning through Optimization of Tree Placements (Papers Track)
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Abstract: Climate change is increasing the intensity and frequency of many extreme weather events, including heatwaves, which results in increased thermal discomfort and mortality rates. While global mitigation action is undoubtedly necessary, so is climate adaptation, e.g., through climate-sensitive urban planning. Among the most promising strategies is harnessing the benefits of urban trees in shading and cooling pedestrian-level environments. Our work investigates the challenge of optimal placement of such trees. Physical simulations can estimate the radiative and thermal impact of trees on human thermal comfort but induce high computational costs. This rules out optimization of tree placements over large areas and considering effects over longer time scales. Hence, we employ neural networks to simulate the point-wise mean radiant temperatures--a driving factor of outdoor human thermal comfort--across various time scales, spanning from daily variations to extended time scales of heatwave events and even decades. To optimize tree placements, we harness the innate local effect of trees within the iterated local search framework with tailored adaptations. We show the efficacy of our approach across a wide spectrum of study areas and time scales. We believe that our approach is a step towards empowering decision-makers, urban designers and planners to proactively and effectively assess the potential of urban trees to mitigate heat stress.

Authors: Simon Schrodi (University of Freiburg); Ferdinand Briegel (University of Freiburg); Max J. Argus (University Of Freiburg); Andreas Christen (University of Freiburg); Thomas Brox (University of Freiburg)

Cities & Urban Planning
NeurIPS 2023 Reinforcement Learning for Wildfire Mitigation in Simulated Disaster Environments (Papers Track)
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Abstract: Climate change has resulted in a year over year increase in adverse weather and weather conditions which contribute to increasingly severe fire seasons. Without effective mitigation, these fires pose a threat to life, property, ecology, cultural heritage, and critical infrastructure. To better prepare for and react to the increasing threat of wildfires, more accurate fire modelers and mitigation responses are necessary. In this paper, we introduce SimFire, a versatile wildland fire projection simulator designed to generate realistic wildfire scenarios, and SimHarness, a modular agent-based machine learning wrapper capable of automatically generating land management strategies within SimFire to reduce the overall damage to the area. Together, this publicly available system allows researchers and practitioners the ability to emulate and assess the effectiveness of firefighter interventions and formulate strategic plans that prioritize value preservation and resource allocation optimization. The repositories are available for download at https://github.com/mitrefireline.

Authors: Alexander Tapley (The MITRE Corporation); savanna o smith (MITRE); Tim Welsh (The MITRE Corporation); Aidan Fennelly (The MITRE Corporation); Dhanuj M Gandikota (The MITRE Corporation); Marissa Dotter (MITRE Corporation); Michael Doyle (The MITRE Corporation); Michael Threet (MITRE)

Reinforcement Learning Disaster Management and Relief
NeurIPS 2023 Combining deep generative models with extreme value theory for synthetic hazard simulation: a multivariate and spatially coherent approach (Papers Track)
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Abstract: Climate hazards can cause major disasters when they occur simultaneously as compound hazards. To understand the distribution of climate risk and inform adaptation policies, scientists need to simulate a large number of physically realistic and spatially coherent events. Current methods are limited by computational constraints and the probabilistic spatial distribution of compound events is not given sufficient attention. The bottleneck in current approaches lies in modelling the dependence structure between variables, as inference on parametric models suffers from the curse of dimensionality. Generative adversarial networks (GANs) are well-suited to such a problem due to their ability to implicitly learn the distribution of data in high-dimensional settings. We employ a GAN to model the dependence structure for daily maximum wind speed, significant wave height, and total precipitation over the Bay of Bengal, combining this with traditional extreme value theory for controlled extrapolation of the tails. Once trained, the model can be used to efficiently generate thousands of realistic compound hazard events, which can inform climate risk assessments for climate adaptation and disaster preparedness. The method developed is flexible and transferable to other multivariate and spatial climate datasets.

Authors: Alison M Peard (University of Oxford); Jim Hall (University of Oxford)

Societal Adaptation & Resilience Generative Modeling
NeurIPS 2023 Uncertainty Quantified Machine Learning for Street Level Flooding Predictions in Norfolk, Virginia (Papers Track)
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Abstract: Everyday citizens, emergency responders, and critical infrastructure can be dramatically affected by the flooding of streets and roads. Climate change exacerbates these floods through sea level rise and more frequent major storm events. Low-level flooding, such as nuisance flooding, continues to increase in frequency, especially in cities like Norfolk, Virginia, which can expect nearly 200 flooding events by 2050 [1]. Recently, machine learning (ML) models have been leveraged to produce real-time predictions based on local weather and geographic conditions. However, ML models are known to produce unusual results when presented with data that varies from their training set. For decision-makers to determine the trustworthiness of the model's predictions, ML models need to quantify their prediction uncertainty. This study applies Deep Quantile Regression to a previously published, Long Short-Term Memory-based model for hourly water depth predictions [2], and analyzes its out-of-distribution performance.

Authors: Steven Goldenberg (Thomas Jefferson National Accelerator Facility); Diana McSpadden (Thomas Jefferson National Accelerator Facility); Binata Roy (University of Virginia); Malachi Schram (Thomas Jefferson National Accelerator Facility); Jonathan Goodall (University of Virginia); Heather Richter (Old Dominion University)

Climate Science & Modeling Uncertainty Quantification & Robustness
NeurIPS 2023 Weakly-semi-supervised object detection in remotely sensed imagery (Papers Track)
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Abstract: Deep learning for detecting objects in remotely sensed imagery can enable new technologies for important applications including mitigating climate change. However, these models often require large datasets labeled with bounding box annotations which are expensive to curate, prohibiting the development of models for new tasks and geographies. To address this challenge, we develop weakly-semi-supervised object detection (WSSOD) models on remotely sensed imagery which can leverage a small amount of bounding boxes together with a large amount of point labels that are easy to acquire at scale in geospatial data. We train WSSOD models which use large amounts of point-labeled images with varying fractions of bounding box labeled images in FAIR1M and a wind turbine detection dataset, and demonstrate that they substantially outperform fully supervised models trained with the same amount of bounding box labeled images on both datasets. Furthermore, we find that the WSSOD models trained with 2-10x fewer bounding box labeled images can perform similarly to or outperform fully supervised models trained on the full set of bounding-box labeled images. We believe that the approach can be extended to other remote sensing tasks to reduce reliance on bounding box labels and increase development of models for impactful applications.

Authors: Ji Hun Wang (Stanford University); Jeremy Irvin (Stanford University); Beri Kohen Behar (Stanford University); Ha Tran (Stanford University); Raghav Samavedam (Stanford University); Quentin Hsu (Stanford University); Andrew Ng (Stanford University)

Earth Observation & Monitoring Unsupervised & Semi-Supervised Learning
NeurIPS 2023 Hybridizing Physics and Neural ODEs for Predicting Plasma Inductance Dynamics in Tokamak Fusion Reactors (Papers Track)
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Abstract: While fusion reactors known as tokamaks hold promise as a firm energy source, advances in plasma control, and handling of events where control of plasmas is lost, are needed for them to be economical. A significant bottleneck towards applying more advanced control algorithms is the need for better plasma simulation, where both physics-based and data-driven approaches currently fall short. The former is bottle-necked by both computational cost and the difficulty of modelling plasmas, and the latter is bottle-necked by the relative paucity of data. To address this issue, this work applies the neural ordinary differential equations (ODE) framework to the problem of predicting a subset of plasma dynamics, namely the coupled plasma current and internal inductance dynamics. As the neural ODE framework allows for the natural inclusion of physics-based inductive biases, we train both physics-based and neural network models on data from the Alcator C-Mod fusion reactor and find that a model that combines physics-based equations with a neural ODE performs better than both existing physics-motivated ODEs and a pure neural ODE model.

Authors: Allen Wang (MIT); Cristina Rea (MIT); Darren Garnier (MIT)

Hybrid Physical Models Reinforcement Learning
NeurIPS 2023 Explainable Offline-online Training of Neural Networks for Multi-scale Climate Modeling (Papers Track)
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Abstract: In global climate models, small-scale physical processes are represented using subgrid-scale (SGS) models known as parameterizations, and these parameterizations contribute substantially to uncertainties in climate projections. Recently, machine learning techniques, particularly deep neural networks (NNs), have emerged as novel tools for developing SGS parameterizations. Different strategies exist for training these NN-based SGS models. Here, we use a 1D model of the quasi-biennial oscillation (QBO) and atmospheric gravity wave (GW) parameterizations as testbeds to explore various learning strategies and challenges due to scarcity of high-fidelity training data. We show that a 12-layer convolutional NN that predicts GW forcings for given wind profiles, when trained offline in a big-data regime (100-years), produces realistic QBOs once coupled to the 1D model. In contrast, offline training of this NN in a small-data regime (18-months) yields unrealistic QBOs. However, online re-training of just two layers of this NN using ensemble Kalman inversion and only time-averaged QBO statistics leads to parameterizations that yield realistic QBOs. Fourier analysis of these three NNs’ kernels suggests how/why re-training works and reveals that these NNs primarily learn low-pass, high-pass, and a combination of band-pass Gabor filters, consistent with the importance of both local and non-local dynamics in GW propagation/dissipation. These strategies/findings apply to data-driven parameterizations of other climate processes generally.

Authors: Hamid Alizadeh Pahlavan (Rice University); Pedram Hassanzadeh (Rice University); M. Joan Alexander (NorthWest Research Associates)

Hybrid Physical Models Unsupervised & Semi-Supervised Learning
NeurIPS 2023 Southern Ocean Dynamics Under Climate Change: New Knowledge Through Physics-Guided Machine Learning (Papers Track)
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Abstract: Complex ocean systems such as the Antarctic Circumpolar Current play key roles in the climate, and current models predict shifts in their strength and area under climate change. However, the physical processes underlying these changes are not well understood, in part due to the difficulty of characterizing and tracking changes in ocean physics in complex models. Using the Antarctic Circumpolar Current as a case study, we extend the method Tracking global Heating with Ocean Regimes (THOR) to a mesoscale eddy permitting climate model and identify regions of the ocean characterized by similar physics, called dynamical regimes, using readily accessible fields from climate models. To this end, we cluster grid cells into dynamical regimes and train an ensemble of neural networks, allowing uncertainty quantification, to predict these regimes and track them under climate change. Finally, we leverage this new knowledge to elucidate the dynamical drivers of the identified regime shifts as noted by the neural network using the `explainability' methods SHAP and Layer-wise Relevance Propagation. A region undergoing a profound shift is where the Antarctic Circumpolar Current intersects the Pacific-Antarctic Ridge, an area important for carbon draw-down and fisheries. In this region, THOR specifically reveals a shift in dynamical regime under climate change driven by changes in wind stress and interactions with bathymetry. Using this knowledge to guide further exploration, we find that as the Antarctic Circumpolar Current shifts north under intensifying wind stress, the dominant dynamical role of bathymetry weakens and the flow intensifies.

Authors: William J Yik (Harvey Mudd College); Maike Sonnewald (University of California, Davis); Mariana Clare (ECMWF); Redouane Lguensat (IPSL)

Oceans & Marine Systems Interpretable ML
NeurIPS 2023 Prototype-oriented Unsupervised Change Detection for Disaster Management (Papers Track)
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Abstract: Climate change has led to an increased frequency of natural disasters such as floods and cyclones. This emphasizes the importance of effective disaster monitoring. In response, the remote sensing community has explored change detection methods. These methods are primarily categorized into supervised techniques, which yield precise results but come with high labeling costs, and unsupervised techniques, which eliminate the need for labeling but involve intricate hyperparameter tuning. To address these challenges, we propose a novel unsupervised change detection method named Prototype-oriented Unsupervised Change Detection for Disaster Management (PUCD). PUCD captures changes by comparing features from pre-event, post-event, and prototype-oriented change synthesis images via a foundational model, and refines results using the Segment Anything Model (SAM). Although PUCD is an unsupervised change detection, it does not require complex hyperparameter tuning. We evaluate PUCD framework on the LEVIR-Extension dataset and the disaster dataset and it achieves state-of-the-art performance compared to other methods on the LEVIR-Extension dataset.

Authors: YoungTack Oh (SI Analytics); Minseok Seo (si-analytics); Doyi Kim (SI Analytics); Junghoon Seo (SI Analytics)

Disaster Management and Relief Earth Observation & Monitoring
NeurIPS 2023 How to Recycle: General Vision-Language Model without Task Tuning for Predicting Object Recyclability (Papers Track)
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Abstract: Waste segregation and recycling place a crucial role in fostering environmental sustainability. However, discerning the whether a material is recyclable or not poses a formidable challenge, primarily because of inadequate recycling guidelines to accommodate a diverse spectrum of objects and their varying conditions. We investigated the role of vision-language models in addressing this challenge. We curated a dataset consisting >1000 images across 11 disposal categories for optimal discarding and assessed the applicability of general vision-language models for recyclability classification. Our results show that Contrastive Language-Image Pre- training (CLIP) model, which is pretrained to understand the relationship between images and text, demonstrated remarkable performance in the zero-shot recycla- bility classification task, with an accuracy of 89%. Our results underscore the potential of general vision-language models in addressing real-world challenges, such as automated waste sorting, by harnessing the inherent associations between visual and textual information.

Authors: Eliot Park (Harvard Medical School); Eddy Pan (Harvard Medical School); Shreya Johri (Harvard Medical School); Pranav Rajpurkar (Harvard Medical School)

Sustainability Natural Language Processing
NeurIPS 2023 Flowering Onset Detection: Traditional Learning vs. Deep Learning Performance in a Sparse Label Context (Papers Track)
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Abstract: Detecting temporal shifts in plant flowering times is of increasing importance in a context of climate change, with applications in plant ecology, but also health, agriculture, and ecosystem management. However, scaling up plant-level monitoring is cost prohibitive, and flowering transitions are complex and difficult to model. We develop two sets of approaches to detect the onset of flowering at large-scale and high-resolution. Using fine grain temperature data with domain knowledge based features and traditional machine learning models provides the best performance. Using satellite data, with deep learning to deal with high dimensionality and transfer learning to overcome ground truth label sparsity, is a challenging but promising approach, as it reaches good performance with more systematically available data.

Authors: Mauricio Soroco (University of British Columbia); Joel Hempel (University of British Columbia); Xinze Xiong (University of British Columbia); Mathias Lécuyer (University of British Columbia); Joséphine Gantois (University of British Columbia)

Computer Vision & Remote Sensing Earth Observation & Monitoring
NeurIPS 2023 Zero shot microclimate prediction with deep learning (Papers Track)
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Abstract: While weather station data is a valuable resource for climate prediction, its reliability can be limited in remote locations. Furthermore, making local predictions often relies on sensor data that may not be accessible for a new, unmonitored location. In response to these challenges, we introduce a novel zero-shot learning approach designed to forecast various climate measurements at new and unmonitored locations. Our method surpasses conventional weather forecasting techniques in predicting microclimate variables by leveraging knowledge extracted from other geographic locations.

Authors: Iman Deznabi (UMass); Peeyush Kumar (Microsoft Research); Madalina Fiterau (University of Massachusetts Amherst)

Meta- and Transfer Learning Climate Science & Modeling
NeurIPS 2023 Glacier Movement Prediction with Attention-based Recurrent Neural Networks and Satellite Data (Papers Track)
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Abstract: Studying glacier movements is crucial because of their indications for global climate change and its effects on local land masses. Building on established methods for glacier movement prediction from Landsat-8 satellite imaging data, we develop an attention-based deep learning model for time series data prediction of glacier movements. In our approach, the Normalized Difference Snow Index is calculated from the Landsat-8 spectral reflectance bands for data of the Parvati Glacier (India) to quantify snow and ice in the scene images, which is then used for time series prediction. Based on this data, a newly developed Long-Short Term Memory Encoder-decoder neural network model is trained, incorporating a Multi-head Self Attention mechanism in the decoder. The model shows promising results, making the prediction of optical flow vectors from pure model predictions possible.

Authors: Jonas Müller (University of Tübingen); Raphael Braun (University of Tübingen); Hendrik P. A. Lensch (University of Tübingen); Nicole Ludwig (University of Tübingen)

Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023 A Causal Discovery Approach To Learn How Urban Form Shapes Sustainable Mobility Across Continents (Papers Track)
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Abstract: For low carbon transport planning it's essential to grasp the location-specific cause-and-effect mechanisms that the built environment has on travel. Yet, current research falls short in representing causal relationships between the "6D" urban form variables and travel, generalizing across different regions, and modelling urban form effects at high spatial resolution. Here, we address these gaps by utilizing a causal discovery and an explainable machine learning framework to detect urban form effects on intra-city travel emissions based on high-resolution mobility data of six cities across three continents. We show that distance to center, demographics and density indirectly affect other urban form features and that location-specific influences align across cities, yet vary in magnitude. In addition, the spread of the city and the coverage of jobs across the city are the strongest determinants of travel-related emissions, highlighting the benefits of compact development and associated benefits. Our work is a starting point for location-specific analysis of urban form effects on mobility using causal discovery approaches, which is highly relevant municipalities across continents.

Authors: Felix Wagner (TU Berlin, MCC Berlin); Florian Nachtigall (MCC Berlin); Lukas B Franken (University of Edinburgh); Nikola Milojevic-Dupont (Mercator Research Institute on Global Commons and Climate Change (MCC)); Marta C. González (Berkeley); Jakob Runge (TU Berlin); Rafael Pereira (IPEA); Felix Creutzig (Mercator Research Institute on Global Commons and Climate Change (MCC))

Cities & Urban Planning Causal & Bayesian Methods
NeurIPS 2023 Unleashing the Autoconversion Rates Forecasting: Evidential Regression from Satellite Data (Papers Track)
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Abstract: High-resolution simulations such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM) can be used to understand the interactions between aerosols, clouds, and precipitation processes that currently represent the largest source of uncertainty involved in determining the radiative forcing of climate change. Nevertheless, due to the exceptionally high computing cost required, this simulation-based approach can only be employed for a short period of time within a limited area. Despite the fact that machine learning can mitigate this problem, the related model uncertainties may make it less reliable. To address this, we developed a neural network (NN) model powered with evidential learning to assess the data and model uncertainties applied to satellite observation data. Our study focuses on estimating the rate at which small droplets (cloud droplets) collide and coalesce to become larger droplets (raindrops) – autoconversion rates -- since this is one of the key processes in the precipitation formation of liquid clouds, hence crucial to better understanding cloud responses to anthropogenic aerosols. The results of estimating the autoconversion rates demonstrate that the model performs reasonably well, with the inclusion of both aleatoric and epistemic uncertainty estimation, which improves the credibility of the model and provides useful insights for future improvement.

Authors: Maria C Novitasari (University College London); Johannes Quaas (Universität Leipzig); Miguel Rodrigues (University College London)

Climate Science & Modeling Uncertainty Quantification & Robustness
NeurIPS 2023 Detailed Glacier Area Change Analysis in the European Alps with Deep Learning (Papers Track)
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Abstract: Glacier retreat is a key indicator of climate change and requires regular updates of the glacier area. Recently, the release of a new inventory for the European Alps showed that glaciers continued to retreat at about 1.3% per year from 2003 to 2015. The outlines were produced by manually correcting the results of a semi-automatic method applied to Sentinel-2 imagery. In this work we develop a fully-automatic pipeline based on Deep Learning to investigate the evolution of the glaciers in the Alps from 2015 to present (2023). After outlier filtering, we provide individual estimates for around 1300 glaciers, representing 87% of the glacierized area. Regionally we estimate an area loss of -1.8% per year, with large variations between glaciers. Code and data are available at https://github.com/dcodrut/glacier_mapping_alps_tccml.

Authors: Codrut-Andrei Diaconu (DLR); Jonathan Bamber (Technical University of Munich)

Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023 An LSTM-based Downscaling Framework for Australian Precipitation Projections (Papers Track)
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Abstract: Understanding potential changes in future rainfall and their local impacts on Australian communities can inform adaptation decisions worth billions of dollars in insurance, agriculture, and other sectors. This understanding relies on downscaling a large ensemble of coarse Global Climate Models (GCMs), our primary tool for simulating future climate. However, the prohibitively high computational cost of downscaling has been a significant barrier. In response, this study develops a cost-efficient downscaling framework for daily precipitation using Long Short-Term Memory (LSTM) models. The models are trained with ERA5 reanalysis data and a customized quantile loss function to better capture precipitation extremes. The framework is employed to downscale precipitation from a GCM member of the CMIP6 ensemble. We demonstrate the skills of the downscaling models to capture spatial and temporal characteristics of precipitation. We also explore regional future changes in precipitation extremes projected by the downscaled GCM. In general, this framework will enable the generation of a large ensemble of regional future projections for Australian rainfall. This will further enhance the assessment of likely climate risks and the quantification of their uncertainties.

Authors: Matthias Bittner (Vienna University of Technology); Sanaa Hobeichi (The University of New South Wales); Muhammad Zawish (Walton Institute, WIT); Samo DIATTA (Assane Seck University of Ziguinchor); Remigius Ozioko (University of Nigeria); Sharon Xu (Indigo Ag); Axel Jantsch (TU Wien)

Extreme Weather Time-series Analysis
NeurIPS 2023 A machine learning pipeline for automated insect monitoring (Papers Track)
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Abstract: Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosystem services on which human society depends. Data on insect abundance, however, remains woefully inadequate. Camera traps, conventionally used for monitoring terrestrial vertebrates, are now being modified for insects, especially moths. We describe a complete, open-source machine learning-based software pipeline for automated monitoring of moths via camera traps, including object detection, moth/non-moth classification, fine-grained identification of moth species, and tracking individuals. We believe that our tools, which are already in use across three continents, represent the future of massively scalable data collection in entomology.

Authors: Aditya Jain (Mila); Fagner Cunha (Federal University of Amazonas); Michael Bunsen (Mila, eButterfly); Léonard Pasi (EPFL); Anna Viklund (Daresay); Maxim Larrivee (Montreal Insectarium); David Rolnick (McGill University, Mila)

Ecosystems & Biodiversity Computer Vision & Remote Sensing
NeurIPS 2023 Climate Variable Downscaling with Conditional Normalizing Flows (Papers Track)
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Abstract: Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations. This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. This approach allows for a probabilistic interpretation of the predictions, while also capturing the stochasticity inherent in the relationships among fine and coarse spatial scales. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.

Authors: Christina Elisabeth Winkler (Mila); Paula Harder (Mila); David Rolnick (McGill University, Mila)

Climate Science & Modeling Generative Modeling
NeurIPS 2023 Global Coastline Evolution Forecasting from Satellite Imagery using Deep Learning (Papers Track)
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Abstract: Coastal zones are under increasing pressures due to climate change and the increasing population densities in coastal areas around the globe. Our ability to accurately forecast the evolution of the coastal zone is of critical importance to coastal managers in the context of risk assessment and mitigation. Recent advances in artificial intelligence and remote sensing enable the development of automatic large-scale analysis methodologies based on observation data. In this work, we make use of a novel satellite-derived shoreline forecasting dataset and a variant of the common Encoder-Decoder neural network, UNet, in order to predict shoreline change based on spatio-temporal data. We analyze the importance of including the spatial context at the prediction step and we find that it greatly enhances model performance. Overall, the model presented here demonstrates significant shoreline forecasting skill around the globe, achieving a global correlation of 0.77.

Authors: Guillaume RIU (Laboratory of Spatial Geophysics and Oceanography Studies); Mahmoud AL NAJAR (Laboratory of Spatial Geophysics and Oceanography Studies); Gregoire THOUMYRE (Laboratory of Spatial Geophysics and Oceanography Studies); Rafael ALMAR (Laboratory of Spatial Geophysics and Oceanography Studies); Dennis Wilson (ISAE)

Oceans & Marine Systems Computer Vision & Remote Sensing
NeurIPS 2023 Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models (Papers Track)
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Abstract: Governments around the world aspire to ground decision-making on evidence. Many of the foundations of policy making — e.g. sensing patterns that relate to societal needs, developing evidence-based programs, forecasting potential outcomes of policy changes, and monitoring effectiveness of policy programs — have the potential to benefit from the use of large-scale datasets or simulations together with intelligent algorithms. These could, if designed and deployed in a way that is well grounded on scientific evidence, enable a more comprehensive, faster, and rigorous approach to policy making. Integrated Assessment Models (IAM) is a broad umbrella covering scientific models that attempt to link main features of society and economy with the biosphere into one modelling framework. At present, these systems are probed by by policy makers and advisory groups in a hypothesis-driven manner. In this paper, we empirically demonstrate that modern Reinforcement Learning can be used to probe IAMs and explore the space of solutions in a more principled manner. While the implication of our results are modest since the environment is simplistic, we believe that this is a stepping stone towards more ambitious use cases, which could allow for effective exploration of policies and understanding of their consequences and limitations.

Authors: Theodore LM Wolf (Carbon Re); Nantas Nardelli (CarbonRe); John Shawe-Taylor (University College London); Maria Perez-Ortiz (University College London)

Public Policy Societal Adaptation & Resilience
NeurIPS 2023 IceCloudNet: Cirrus and mixed-phase cloud prediction from SEVIRI input learned from sparse supervision (Papers Track)
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Abstract: Clouds containing ice particles play a crucial role in the climate system. Yet they remain a source of great uncertainty in climate models and future climate projections. In this work, we create a new observational constraint of regime-dependent ice microphysical properties at the spatio-temporal coverage of geostationary satellite instruments and the quality of active satellite retrievals. We achieve this by training a convolutional neural network on three years of SEVIRI and DARDAR data sets. This work will enable novel research to improve ice cloud process understanding and hence, reduce uncertainties in a changing climate and help assess geoengineering methods for cirrus clouds.

Authors: Kai Jeggle (ETH Zurich); Mikolaj Czerkawski (ESA); Federico Serva (European Space Agency, Italian Space Agency); Bertrand Le Saux (European Space Agency (ESA)); David Neubauer (ETH Zurich); Ulrike Lohmann (ETH Zurich)

Climate Science & Modeling Computer Vision & Remote Sensing
NeurIPS 2023 SAM-CD: Change Detection in Remote Sensing Using Segment Anything Model (Papers Track)
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Abstract: In remote sensing, Change Detection (CD) refers to locating surface changes in the same area over time. Changes can occur due to man-made or natural activities, and CD is important for analyzing climate changes. The recent advancements in satellite imagery and deep learning allow the development of affordable and powerful CD solutions. The breakthroughs in computer vision Foundation Models (FMs) bring new opportunities for better and more flexible remote sensing solutions. However, solving CD using FMs has not been explored before and this work presents the first FM-based deep learning model, SAM-CD. We propose a novel model that adapts the Segment Anything Model (SAM) for solving CD. The experimental results show that the proposed approach achieves the state of the art when evaluated on two challenging benchmark public datasets LEVIR-CD and DSIFN-CD.

Authors: Faroq ALTam (Elm Company); Thariq Khalid (Elm Company); Athul Mathew (Elm Company); Andrew Carnell (Elm Company); Riad Souissi (Elm Company)

Computer Vision & Remote Sensing Cities & Urban Planning
NeurIPS 2023 Segment-then-Classify: Few-shot instance segmentation for environmental remote sensing (Papers Track)
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Abstract: Instance segmentation is pivotal for environmental sciences and climate change research, facilitating important tasks from land cover classification to glacier monitoring. This paper addresses the prevailing challenges associated with data scarcity when using traditional models like YOLOv8 by introducing a novel, data-efficient workflow for instance segmentation. The proposed Segment-then-Classify (STC) strategy leverages the zero-shot capabilities of the novel Segment Anything Model (SAM) to segment all objects in an image and then uses a simple classifier such as the Vision Transformer (ViT) to identify objects of interest thereafter. Evaluated on the VHR-10 dataset, our approach demonstrated convergence with merely 40 examples per class. YOLOv8 requires 3 times as much data to achieve the STC's peak performance. The highest performing class in the VHR-10 dataset achieved a near-perfect mAP@0.5 of 0.99 using the STC strategy. However, performance varied greatly across other classes due to the SAM model’s occasional inability to recognize all relevant objects, indicating a need for refining the zero-shot segmentation step. The STC workflow therefore holds promise for advancing few-shot learning for instance segmentation in environmental science.

Authors: Yang Hu (University of California, Santa Barbara); Kelly Caylor (UCSB); Anna S Boser (UCSB)

Computer Vision & Remote Sensing Earth Observation & Monitoring
NeurIPS 2023 Surrogate modeling based History Matching for an Earth system model of intermediate complexity (Papers Track)
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Abstract: Climate General Circulation Models (GCMs) constitute the primary tools for climate projections that inform IPCC Assessment Reports. Calibrating, or tuning the parameters of the models can significantly improve their predictions, thus their scientific and societal impacts. Unfortunately, traditional tuning techniques remain time-consuming and computationally costly, even at coarse resolution. A specific challenge for the tuning of climate models lies in the tuning of both fast and slow climatic features: while atmospheric processes adjust on hourly to weekly timescales, vegetation or ocean dynamics drive mechanisms of variability at decadal to millenial timescales. In this work, we explore whether and how History Matching, which uses machine learning based emulators to accelerate and automate the tuning process, is relevant for tuning climate models with multiple timescales. To facilitate this exploration, we work with a climate model of intermediate complexity, yet test experimental tuning protocols that can be directly applied to more complex GCMs to reduce uncertainty in climate projections.

Authors: Maya Janvier (Centrale Supélec); Redouane Lguensat (IPSL); Julie Deshayes (LOCEAN IPSL); Aurélien Quiquet (LSCE); Didier Roche (LSCE); V. Balaji (Schmidt Futures)

Climate Science & Modeling
NeurIPS 2023 FireSight: Short-Term Fire Hazard Prediction Based on Active Fire Remote Sensing Data (Papers Track)
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Abstract: Wildfires are becoming unpredictable natural hazards in many regions due to climate change. However, existing state-of-the-art wildfire forecasting tools, such as the Fire Weather Index (FWI), rely solely on meteorological input parameters and have limited ability to model the increasingly dynamic nature of wildfires. In response to the escalating threat, our work addresses this shortcoming in short-term fire hazard prediction. First, we present a comprehensive and high fidelity remotely sensed active fire dataset fused from over 20 satellites. Second, we develop region-specific ML-based 3-7 day wildfire hazard prediction models for hazard South America, Australia, and Southern Europe. The different models cover pixel-wise, spatial and spatio-temporal architectures, and utilize weather, fuel and location data. We evaluate the models using time-based cross-validation and can show superior performance with a PR-AUC score up to 44 times higher compared to the baseline FWI model. Using explainable AI methods, we show that these data-driven models are also capable of learning meaningful physical patterns and inferring region-specific wildfire drivers.

Authors: Julia Gottfriedsen (OroraTech GmbH); Johanna Strebl (OroraTech GmbH); Max Berrendorf (Ludwig-Maximilians-Universität München); Martin Langer (OroraTech GmbH); Volker Tresp (Ludwig-Maximilians-Universität München)

Disaster Management and Relief Extreme Weather
NeurIPS 2023 Scaling Sodium-ion Battery Development with NLP (Papers Track)
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Abstract: Sodium-ion batteries (SIBs) have been gaining attention for applications like grid-scale energy storage, largely owing to the abundance of sodium and an expected favorable $/kWh figure. SIBs can leverage the well-established manufacturing knowledge of Lithium-ion Batteries (LIBs), but several materials synthesis and performance challenges for electrode materials need to be addressed. This work extracts a large database of challenges restricting the performance and synthesis of SIB cathode active materials (CAMs) and pairs them with corresponding mitigation strategies from the SIB literature by employing custom natural language processing (NLP) tools. The derived insights enable scientists in research and industry to navigate a large number of proposed strategies and focus on impactful scalability-informed mitigation strategies to accelerate the transition from lab to commercialization.

Authors: Mrigi Munjal (Massachusetts Institute of Technology); Thorben Pein (TU Munich); Vineeth Venugopal (Massachusetts Institute of Technology); Kevin Huang (Massachusetts Institute of Technology); Elsa Olivetti (Massachusetts Institute of Technology)

Chemistry & Materials Power & Energy
NeurIPS 2023 The built environment and induced transport CO2 emissions: A double machine learning approach to account for residential self-selection (Papers Track)
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Abstract: Understanding why travel behavior differs between residents of urban centers and suburbs is key to sustainable urban planning. Especially in light of rapid urban growth, identifying housing locations that minimize travel demand and induced CO2 emissions is crucial to mitigate climate change. While the built environment plays an important role, the precise impact on travel behavior is obfuscated by residential self-selection. To address this issue, we propose a double machine learning approach to obtain unbiased, spatially-explicit estimates of the effect of the built environment on travel-related CO2 emissions for each neighborhood by controlling for residential self-selection. We examine how socio-demographics and travel-related attitudes moderate the effect and how it decomposes across the 5Ds of the built environment. Based on a case study for Berlin and the travel diaries of 32,000 residents, we find that the built environment causes household travel-related CO2 emissions to differ by a factor of almost two between central and suburban neighborhoods in Berlin. To highlight the practical importance for urban climate mitigation, we evaluate current plans for 64,000 new residential units in terms of total induced transport CO2 emissions. Our findings underscore the significance of spatially differentiated compact development to decarbonize the transport sector.

Authors: Florian Nachtigall (Technical University of Berlin); Felix Wagner (TU Berlin, MCC Berlin); Peter Berrill (Technical University of Berlin); Felix Creutzig (Mercator Research Institute on Global Commons and Climate Change (MCC))

Cities & Urban Planning Causal & Bayesian Methods
NeurIPS 2023 Lightweight, Pre-trained Transformers for Remote Sensing Timeseries (Papers Track)
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Abstract: Machine learning models for parsing remote sensing data have a wide range of societally relevant applications, but labels used to train these models can be difficult or impossible to acquire. This challenge has spurred research into self-supervised learning for remote sensing data. Current self-supervised learning approaches for remote sensing data draw significant inspiration from techniques applied to natural images. However, remote sensing data has important differences from natural images -- for example, the temporal dimension is critical for many tasks and data is collected from many complementary sensors. We show we can create significantly smaller performant models by designing architectures and self-supervised training techniques specifically for remote sensing data. We introduce the Pretrained Remote Sensing Transformer (Presto), a transformer-based model pre-trained on remote sensing pixel-timeseries data. Presto excels at a wide variety of globally distributed remote sensing tasks and performs competitively with much larger models while requiring far less compute. Presto can be used for transfer learning or as a feature extractor for simple models, enabling efficient deployment at scale.

Authors: Gabriel Tseng (NASA Harvest); Ruben Cartuyvels (KULeuven); Ivan Zvonkov (University of Maryland); Mirali Purohit (Arizona State University (ASU)); David Rolnick (McGill University, Mila); Hannah R Kerner (Arizona State University)

Earth Observation & Monitoring Unsupervised & Semi-Supervised Learning
NeurIPS 2023 Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology (Papers Track)
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Abstract: An invasive species of grass known as "buffelgrass" contributes to severe wildfires and biodiversity loss in the Southwest United States. We tackle the problem of predicting buffelgrass "green-ups" (i.e. readiness for herbicidal treatment). To make our predictions, we explore temporal, visual and multi-modal models that combine satellite sensing and deep learning. We find that all of our neural-based approaches improve over conventional buffelgrass green-up models, and discuss how neural model deployment promises significant resource savings.

Authors: Lucas Rosenblatt (NYU); Bin Han (University of Washington); Erin Posthumus (USA NPN); Theresa Crimmins (USA NPN); Bill G Howe (University of Washington)

Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023 Typhoon Intensity Prediction with Vision Transformer (Papers Track)
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Abstract: Predicting typhoon intensity accurately across space and time is crucial for issuing timely disaster warnings and facilitating emergency response. This has vast potential for minimizing life losses and property damages as well as reducing economic and environmental impacts. Leveraging satellite imagery for scenario analysis is effective but also introduces additional challenges due to the complex relations among clouds and the highly dynamic context. Existing deep learning methods in this domain rely on convolutional neural networks (CNNs), which suffer from limited per-layer receptive fields. This limitation hinders their ability to capture long-range dependencies and global contextual knowledge during inference. In response, we introduce a novel approach, namely "Typhoon Intensity Transformer" (Tint), which leverages self-attention mechanisms with global receptive fields per layer. Tint adopts a sequence-to-sequence feature representation learning perspective. It begins by cutting a given satellite image into a sequence of patches and recursively employs self-attention operations to extract both local and global contextual relations between all patch pairs simultaneously, thereby enhancing per-patch feature representation learning. Extensive experiments on a publicly available typhoon benchmark validate the efficacy of Tint in comparison with both state-of-the-art deep learning and conventional meteorological methods. Our code is available at https://github.com/chen-huanxin/Tint.

Authors: Huanxin Chen (South China University of Technology); Pengshuai Yin (South China University of Technology); Huichou Huang (City University of Hong Kong); Qingyao Wu (South China University of Technology); Ruirui Liu (Brunel University London); Xiatian Zhu (University of Surrey)

Computer Vision & Remote Sensing Climate Science & Modeling
NeurIPS 2023 Data Assimilation using ERA5, ASOS, and the U-STN model for Weather Forecasting over the UK (Papers Track)
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Abstract: In recent years, the convergence of data-driven machine learning models with Data Assimilation (DA) offers a promising avenue for enhancing weather forecasting. This study delves into this emerging trend, presenting our methodologies and outcomes. We harnessed the UK's local ERA5 850 hPa temperature data and refined the U-STN12 global weather forecasting model, tailoring its predictions to the UK's climate nuances. From the ASOS network, we sourced t2m data, representing ground observations across the UK. We employed the advanced kriging method with a polynomial drift term for consistent spatial resolution. Furthermore, Gaussian noise was superimposed on the ERA5 T850 data, setting the stage for ensuing multi-time step virtual observations. Probing into the assimilation impacts, the ASOS t2m data was integrated with the ERA5 T850 dataset. Our insights reveal that while global forecast models can adapt to specific regions, incorporating atmospheric data in DA significantly bolsters model accuracy. Conversely, the direct assimilation of surface temperature data tends to mitigate this enhancement, tempering the model's predictive prowess.

Authors: WENQI WANG (Imperial College London); Jacob Bieker (Open Climate Fix); Rossella Arcucci (Imperial College London); Cesar Quilodran-Casas (Imperial College London)

Earth Observation & Monitoring Uncertainty Quantification & Robustness
NeurIPS 2023 Stress-testing the coupled behavior of hybrid physics-machine learning climate simulations on an unseen, warmer climate (Papers Track)
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Abstract: Accurate and computationally-viable representations of clouds and turbulence are a long-standing challenge for climate model development. Traditional parameterizations that crudely but efficiently approximate these processes are a leading source of uncertainty in long-term projected warming and precipitation patterns. Machine Learning (ML)-based parameterizations have long been hailed as a promising alternative with the potential to yield higher accuracy at a fraction of the cost of more explicit simulations. However, these ML variants are often unpredictably unstable and inaccurate in online testing (i.e. in a downstream hybrid simulation task where they are dynamically coupled to the large-scale climate model). These issues are exacerbated in out-of-distribution climates. Certain design decisions such as ``climate-invariant" feature transformation, input vector expansion, and temporal history incorporation have been shown to improve online performance, but they may be insufficient for the mission-critical task of online out-of-distribution generalization. If feature selection and transformations can inoculate hybrid physics-ML climate models from non-physical out-of-distribution extrapolation in a changing climate, there is far greater potential in extrapolating from observational data. Otherwise, training on multiple simulated climates becomes an inevitable necessity. While our results show generalization benefits from these design decisions, such benefits do not sufficiently preclude the necessity of using multi-climate simulated training data.

Authors: Jerry Lin (University of California, Irvine); Mohamed Aziz Bhouri (Columbia University); Tom G Beucler (Columbia University & UCI); Sungduk Yu (University of California, Irvine); Michael Pritchard (UCI)

Climate Science & Modeling Hybrid Physical Models
NeurIPS 2023 Deploying Reinforcement Learning based Economizer Optimization at Scale (Papers Track)
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Abstract: Building operations account for a significant portion of global emissions, contributing approximately 28\% of global greenhouse gas emissions. With anticipated increase in cooling demand due to rising global temperatures, the optimization of rooftop units (RTUs) in buildings becomes crucial for reducing emissions. We focus on the optimization of the economizer logic within RTUs, which balances the mix of indoor and outdoor air. By effectively utilizing free outside air, economizers can significantly decrease mechanical energy usage, leading to reduced energy costs and emissions. We introduce a reinforcement learning (RL) approach that adaptively controls the economizer based on the unique characteristics of individual facilities. We have trained and deployed our solution in the real-world across a distributed building stock. We address the scaling challenges with our cloud-based RL deployment on 10K+ RTUs across 200+ sites.

Authors: Ivan Cui (Amazon); Wei Yih Yap (Amazon); Charles Prosper (Independant); Bharathan Balaji (Amazon); Jake Chen (Amazon)

Buildings Reinforcement Learning
NeurIPS 2023 The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation (Papers Track)
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Abstract: Floods can cause horrific harm to life and property. However, they can be mitigated or even avoided by the effective use of hydraulic structures such as dams, gates, and pumps. By pre-releasing water via these structures in advance of extreme weather events, water levels are sufficiently lowered to prevent floods. In this work, we propose FIDLAR, a Forecast Informed Deep Learning Architecture, achieving flood management in watersheds with hydraulic structures in an optimal manner by balancing out flood mitigation and unnecessary wastage of water via pre-releases. We perform experiments with FIDLAR using data from the South Florida Water Management District, which manages a coastal area that is highly prone to frequent storms and floods. Results show that FIDLAR performs better than the current state-of-the-art with several orders of magnitude speedup and with provably better pre-release schedules. The dramatic speedups make it possible for FIDLAR to be used for real-time flood management. The main contribution of this paper is the effective use of tools for model explainability, allowing us to understand the contribution of the various environmental factors towards its decisions.

Authors: Jimeng Shi (Florida International University); Vitalii Stebliankin (FIU); Giri Narasimhan (Florida International University)

Disaster Management and Relief Interpretable ML
NeurIPS 2023 Surrogate Neural Networks to Estimate Parametric Sensitivity of Ocean Models (Papers Track)
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Abstract: Modeling is crucial to understanding the effect of greenhouse gases, warming, and ice sheet melting on the ocean. At the same time, ocean processes affect phenomena such as hurricanes and droughts. Parameters in the models that cannot be physically measured have a significant effect on the model output. For an idealized ocean model, we generate perturbed parameter ensemble data and generate surrogate neural network models. The neural surrogates accurately predicted the one-step forward dynamics, of which we then computed the parametric sensitivity.

Authors: Yixuan Sun (Argonne National Laboratory); Elizabeth Cucuzzella (Tufts University); Steven Brus (Argonne National Laboratory); Sri Hari Krishna Narayanan (Argonne National Laboratory); Balu Nadiga (Los Alamos National Lab); Luke Van Roekel (Los Alamos National Laboratory); Jan Hückelheim (Argonne National Laboratory); Sandeep Madireddy (Argonne National Laboratory)

Oceans & Marine Systems
NeurIPS 2023 Towards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia (Papers Track)
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Abstract: Environmental indicators can play a crucial role in forecasting infectious disease outbreaks, holding promise for community-level interventions. Yet, significant gaps exist in the literature regarding the influence of changes in environmental conditions on disease spread over time and across different regions and climates making it challenging to obtain reliable forecasts. This paper aims to propose an approach to predict malaria incidence over time and space by employing a multi-dimensional long short-term memory model (M-LSTM) to simultaneously analyse environmental indicators such as vegetation, temperature, night-time lights, urban/rural settings, and precipitation. We developed and validated a spatio-temporal data fusion approach to predict district-level malaria incidence rates for the year 2017 using spatio-temporal data from 2000 to 2016 across three South Asian countries: Pakistan, India, and Bangladesh. In terms of predictive performance the proposed M-LSTM model results in lower country-specific error rates compared to existing spatio-temporal deep learning models. The data and code have been made publicly available at the study GitHub repository.

Authors: Usman Nazir (Lahore University of Management Sciences); Ahzam Ejaz (Lahore University of Management Sciences); Muhammad Talha Quddoos (Lahore University of Management Sciences); Momin Uppal (Lahore University of Management Sciences); Sara khalid (University of Oxford)

Health Climate Science & Modeling
NeurIPS 2023 Contextual Reinforcement Learning for Offshore Wind Farm Bidding (Papers Track)
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Abstract: We propose a framework for applying reinforcement learning to contextual two-stage stochastic optimization and apply this framework to the problem of energy market bidding of an off-shore wind farm. Reinforcement learning could potentially be used to learn close to optimal solutions for first stage variables of a two-stage stochastic program under different contexts. Under the proposed framework, these solutions would be learned without having to solve the full two-stage stochastic program. We present initial results of training using the DDPG algorithm and present intended future steps to improve performance.

Authors: David Cole (University of Wisconsin-Madison); Himanshu Sharma (Pacific Northwest National Laboratory); Wei Wang (Pacific Northwest National Laboratory)

Power & Energy Reinforcement Learning
NeurIPS 2023 CityTFT: Temporal Fusion Transformer for Urban Building Energy Modeling (Papers Track)
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Abstract: Urban Building Energy Modeling (UBEM) is an emerging method to investigate urban design and energy systems against the increasing energy demand at urban and neighborhood levels. However, current UBEM methods are mostly physic-based and time-consuming in multiple climate change scenarios. This work proposes CityTFT, a data-driven UBEM framework, to accurately model the energy demands in urban environments. With the empowerment of the underlying TFT framework and an augmented loss function, CityTFT could predict heating and cooling triggers in unseen climate dynamics with an F1 score of 99.98 \% while RMSE of loads of 13571.3750 Wh.

Authors: Ting-Yu Dai (The University of Texas at Austin); Dev Niyogi (The University of Texas at Austin); Zoltan Nagy (The University of Texas at Austin)

Buildings Time-series Analysis
NeurIPS 2023 A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Distributed Converter-based Microgrid Voltage Control (Papers Track)
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Abstract: Renewable energy plays a crucial role in mitigating climate change. With the rising use of distributed energy resources (DERs), microgrids (MGs) have emerged as a solution to accommodate high DER penetration. However, controlling MGs' voltage during islanded operation is challenging due to system's nonlinearity and stochasticity. Although multi-agent reinforcement learning (MARL) methods have been applied to distributed MG voltage control, they suffer from bad scalability and are found difficult to control the MG with a large number of DGs due to the well-known curse of dimensionality. To address this, we propose a scalable network-aware reinforcement learning framework which exploits network structure to truncate the critic's Q-function to achieve scalability. Our experiments show effective control of a MG with up to 84 DGs, surpassing the existing maximum of 40 agents in the existing literature. We also compare our framework with state-of-the-art MARL algorithms to show the superior scalability of our framework.

Authors: Han Xu (Tsinghua University); Guannan Qu (Carnegie Mellon University)

Power & Energy Reinforcement Learning
NeurIPS 2023 Reinforcement Learning in agent-based modeling to reduce carbon emissions in transportation (Papers Track)
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Abstract: This paper explores the integration of reinforcement learning (RL) into transportation simulations to explore system interventions to reduce greenhouse gas emissions. The study leverages the Behavior, Energy, Automation, and Mobility (BEAM) transportation simulation framework in conjunction with the Berkeley Integrated System for Transportation Optimization (BISTRO) for scenario development. The main objective is to determine optimal parameters for transportation simulations to increase public transport usage and reduce individual vehicle reliance. Initial experiments were conducted on a simplified transportation scenario, and results indicate that RL can effectively find system interventions that increase public transit usage and decrease transportation emissions.

Authors: Yuhao Yuan (UC Berkeley); Felipe Leno da Silva (Lawrence Livermore National Laboratory); Ruben Glatt (Lawrence Livermore National Laboratory)

Transportation Reinforcement Learning
NeurIPS 2023 Continuous Convolutional Neural Networks for Disruption Prediction in Nuclear Fusion Plasmas (Papers Track)
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Abstract: Grid decarbonization for climate change requires dispatchable carbon-free energy like nuclear fusion. The tokamak concept offers a promising path for fusion, but one of the foremost challenges in implementation is the occurrence of energetic plasma disruptions. In this study, we delve into Machine Learning approaches to predict plasma state outcomes. Our contributions are twofold: (1) We present a novel application of Continuous Convolutional Neural Networks for disruption prediction and (2) We examine the advantages and disadvantages of continuous models over discrete models for disruption prediction by comparing our model with the previous, discrete state of the art, and show that continuous models offer significantly better performance (Area Under the Receiver Operating Characteristic Curve = 0.974 v.s. 0.799) with fewer parameters.

Authors: William F Arnold (KAIST); Lucas Spangher (MIT PSFC); Cristina Rea (MIT PSFC)

Time-series Analysis Hybrid Physical Models
NeurIPS 2023 Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests (Papers Track)
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Abstract: Understanding the future climate is crucial for informed policy decisions on climate change prevention and mitigation. Earth system models play an important role in predicting future climate, requiring accurate representation of complex sub- processes that span multiple time scales and spatial scales. One such process that links seasonal and interannual climate variability to cyclical biological events is tree phenology in deciduous forests. Phenological dates, such as the start and end of the growing season, are critical for understanding the exchange of carbon and water between the biosphere and the atmosphere. Mechanistic prediction of these dates is challenging. Hybrid modelling, which integrates data-driven approaches into complex models, offers a solution. In this work, as a first step towards this goal, train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models. This highlights the potential of data-driven methods to improve climate predictions. We also analyze which variables and aspects of the time series influence the predicted onset of the season, in order to gain a better understanding of the advantages and limitations of our model.

Authors: Christian Reimers (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry); David Hafezi Rachti (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry); Guohua Liu (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry); Alexander Winkler (Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry)

Climate Science & Modeling Interpretable ML
NeurIPS 2023 RMM-VAE: a machine learning method for identifying probabilistic weather regimes targeted to a local-scale impact variable (Papers Track)
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Abstract: Identifying large-scale atmospheric patterns that modulate extremes in local-scale variables such as precipitation has the potential to improve long-term climate projections as well as extended-range forecasting skill. This paper proposes a novel probabilistic machine learning method, RMM-VAE, based on a variational autoencoder architecture for identifying weather regimes targeted to a local-scale impact variable. The new method is compared to three existing methods in the task of identifying robust weather regimes that are predictive of precipitation over Morocco while capturing the full phase space of atmospheric dynamics over the Mediterranean. RMM-VAE performs well across these different objectives, outperforming linear methods in reconstructing the full phase space and predicting the target variable, highlighting the potential benefit of applying the method to various climate applications such as downscaling and extended-range forecasting.

Authors: Fiona R Spuler (University of Reading); Marlene Kretschmer (Universität Leipzig); Yevgeniya Kovalchuck (University College London); Magdalena Balmaseda (ECMWF); Ted Shepherd (University of Reading)

Generative Modeling Climate Science & Modeling
NeurIPS 2023 Accelerating GHG Emissions Inference: A Lagrangian Particle Dispersion Model Emulator Using Graph Neural Networks (Papers Track)
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Abstract: Inverse modelling systems relying on Lagrangian Particle Dispersion Models (LPDMs) are a popular way to quantify greenhouse gas (GHG) emissions using atmospheric observations, providing independent validation to countries' self-reported emissions. However, the increased volume of satellite measurements cannot be fully leveraged due to computational bottlenecks. Here, we propose a data-driven architecture with Graph Neural Networks that emulates the outputs of LPDMs using only meteorological inputs, and demonstrate it in application with preliminary results for satellite measurements over Brazil.

Authors: Elena Fillola (University of Bristol); Raul Santos Rodriguez (University of Bristol); Matt Rigby (University of Bristol)

Climate Science & Modeling
NeurIPS 2023 Gaussian Processes for Monitoring Air-Quality in Kampala (Papers Track)
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Abstract: Monitoring air pollution is of vital importance to the overall health of the population. Unfortunately, devices that can measure air quality can be expensive, and many cities in low and middle-income countries have to rely on a sparse allocation of them. In this paper, we investigate the use of Gaussian Processes for both nowcasting the current air-pollution in places where there are no sensors and forecasting the air-pollution in the future at the sensor locations. In particular, we focus on the city of Kampala in Uganda, using data from AirQo's network of sensors. We demonstrate the advantage of removing outliers, compare different kernel functions and additional inputs. We also compare two sparse approximations to allow for the large amounts of temporal data in the dataset.

Authors: Clara Stoddart (Imperial College London); Lauren Shrack (Massachusetts Institute of Technology); Usman Abdul-Ganiy (AirQo, Makerere University); Richard Sserunjogi (AirQo, Makerere University); Engineer Bainomugisha (AirQo, Makerere University); Deo Okure (AirQo, Makerere University); Ruth Misener (Imperial College London); Jose Pablo Folch (Imperial College London); Ruby Sedgwick (Imperial College London)

Causal & Bayesian Methods Health
NeurIPS 2023 Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data (Papers Track) Overall Best Paper
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Abstract: Satellite-based remote sensing is instrumental in the monitoring and mitigation of the effects of anthropogenic climate change. Large scale, high resolution data derived from these sensors can be used to inform intervention and policy decision making, but the timeliness and accuracy of these interventions is limited by use of optical data, which cannot operate at night and is affected by adverse weather conditions. Synthetic Aperture Radar (SAR) offers a robust alternative to optical data, but its associated complexities limit the scope of labelled data generation for traditional deep learning. In this work, we apply a self-supervised pretraining scheme, masked autoencoding, to SAR amplitude data covering 8.7\% of the Earth's land surface area, and tune the pretrained weights on two downstream tasks crucial to monitoring climate change - vegetation cover prediction and land cover classification. We show that the use of this pretraining scheme reduces labelling requirements for the downstream tasks by more than an order of magnitude, and that this pretraining generalises geographically, with the performance gain increasing when tuned downstream on regions outside the pretraining set. Our findings significantly advance climate change mitigation by facilitating the development of task and region-specific SAR models, allowing local communities and organizations to deploy tailored solutions for rapid, accurate monitoring of climate change effects.

Authors: Matthew J Allen (University of Cambridge); Francisco Dorr (Independent); Joseph A Gallego (National University Of Colombia); Laura Martínez-Ferrer (University of Valencia); Freddie Kalaitzis (University of Oxford); Raul Ramos-Pollan (Universidad de Antioquia); Anna Jungbluth (European Space Agency)

Computer Vision & Remote Sensing Earth Observation & Monitoring
NeurIPS 2023 Sim2Real for Environmental Neural Processes (Papers Track)
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Abstract: Machine learning (ML)-based weather models have recently undergone rapid improvements.These models are typically trained on gridded reanalysis data from numerical data assimilation systems. However, reanalysis data comes with limitations, such as assumptions about physical laws and low spatiotemporal resolution. The gap between reanalysis and reality has sparked growing interest in training ML models directly on observations such as weather stations. Modelling scattered and sparse environmental observations requires scalable and flexible ML architectures, one of which is the convolutional conditional neural process (ConvCNP). ConvCNPs can learn to condition on both gridded and off-the-grid context data to make uncertainty-aware predictions at target locations. However, the sparsity of real observations presents a challenge for data-hungry deep learning models like the ConvCNP. One potential solution is `Sim2Real': pre-training on reanalysis and fine-tuning on observational data. We analyse Sim2Real with a ConvCNP trained to interpolate surface air temperature over Germany, using varying numbers of weather stations for fine-tuning. On held-out weather stations, Sim2Real training substantially outperforms the same model trained only with reanalysis data or only with station data, showing that reanalysis data can serve as a stepping stone for learning from real observations. Sim2Real could enable more accurate models for climate change monitoring and adaptation.

Authors: Jonas Scholz (University of Cambridge)

Meta- and Transfer Learning Climate Science & Modeling
NeurIPS 2023 Methane Plume Detection with U-Net Segmentation on Sentinel-2 Image Data (Papers Track)
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Abstract: Methane emissions have a significant impact on increasing global warming. Satellite-based methane detection methods can help mitigate methane emissions, as they provide a constant and global detection. The Sentinel-2 constellation, in particular, offers frequent and publicly accessible images on a global scale. We propose a deep learning approach to detect methane plumes from Sentinel-2 images. We construct a dataset of 5200 satellite images with identified methane plumes, on which we train a U-Net model. Preliminary results demonstrate that the model is able to correctly identify methane plumes on training data, although generalization to new methane plumes remains challenging. All code, data, and models are made available online.

Authors: Berenice du Baret (ISAE-Supaero); Simon Finos (ISAE-Supaero); Hugo Guiglion (ISAE-Supaero); Dennis Wilson (ISAE)

Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023 Machine Learning Assisted Bayesian Calibration of Model Physics Parameters for Wetland Methane Emissions: A Case Study at a FLUXNET-CH4 Site (Papers Track)
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Abstract: Methane (CH4) possesses a notably higher warming potential than carbon dioxide despite its lower atmospheric concentration, making it integral to global climate dynamics. Wetlands stand out as the predominant natural contributor to global methane emissions. Accurate modeling of methane emissions from wetlands is crucial for understanding and predicting climate change dynamics. However, such modeling efforts are often constrained by the inherent uncertainties in model parameters. Our work leverages machine learning (ML) to calibrate five physical parameters of the Energy Exascale Earth System Model (E3SM) land model (ELM) to improve the model’s accuracy in simulating wetland methane emissions. Unlike traditional deterministic calibration methods that target a single set of optimal values for each parameter, Bayesian calibration takes a probabilistic approach and enables capturing the inherent uncertainties in complex systems and providing robust parameter distributions for reliable predictions. However, Bayesian calibration requires numerous model runs and makes it computationally expensive. We employed an ML algorithm, Gaussian process regression (GPR), to emulate the ELM’s methane model, which dramatically reduced the computational time from 6 CPU hours to just 0.72 milliseconds per simulation. We exemplified the procedure at a representative FLUXNET-CH4 site (US-PFa) with the longest continuous methane emission data. Results showed that the default values for two of the five parameters examined were not aligned well with their respective posterior distributions, suggesting that the model’s default parameter values might not always be optimal for all sites, and that site-specific analysis is warranted. In particular, analyses at sites with different vegetation types and wetland characteristics could reveal more useful insights for understanding methane emissions modeling.

Authors: Sandeep Chinta (Massachusetts Institute of Technology); Xiang Gao (Massachusetts Institute of Technology); Qing Zhu (Lawrence Berkeley National Laboratory)

Climate Science & Modeling
NeurIPS 2023 ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data (Papers Track)
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Abstract: High-resolution simulations, such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM), provide valuable insights into the complex interactions among aerosols, clouds, and precipitation, which are the major contributors to climate change uncertainty. However, due to their exorbitant computational costs, they can only be employed for a limited period and geographical area. To address this, we propose a more cost-effective method powered by an emerging machine learning approach to better understand the intricate dynamics of the climate system. Our approach involves active learning techniques by leveraging high-resolution climate simulation as an oracle that is queried based on an abundant amount of unlabeled data drawn from satellite observations. In particular, we aim to predict autoconversion rates, a crucial step in precipitation formation, while significantly reducing the need for a large number of labeled instances. In this study, we present novel methods: custom query strategy fusion for labeling instances -- weight fusion (WiFi) and merge fusion (MeFi) -- along with active feature selection based on SHAP. These methods are designed to tackle real-world challenges -- in this case, climate change, with a specific focus on the prediction of autoconversion rates -- due to their simplicity and practicality in application.

Authors: Maria C Novitasari (University College London); Johanness Quaas (Universität Leipzig); Miguel Rodrigues (University College London)

Active Learning Climate Science & Modeling
NeurIPS 2023 Towards Causal Representations of Climate Model Data (Papers Track)
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Abstract: Climate models, such as Earth system models (ESMs), are crucial for simulating future climate change based on projected Shared Socioeconomic Pathways (SSP) greenhouse gas emissions scenarios. While ESMs are sophisticated and invaluable, machine learning-based emulators trained on existing simulation data can project additional climate scenarios much faster and are computationally efficient. However, they often lack generalizability and interpretability. This work delves into the potential of causal representation learning, specifically the Causal Discovery with Single-parent Decoding (CDSD) method, which could render climate model emulation efficient and interpretable. We evaluate CDSD on multiple climate datasets, focusing on emissions, temperature, and precipitation. Our findings shed light on the challenges, limitations, and promise of using CDSD as a stepping stone towards more interpretable and robust climate model emulation.

Authors: Julien Boussard (Columbia University); Chandni Nagda (University of Illinois at Urbana-Champaign); Julia Kaltenborn (McGill University); Charlotte Lange (Mila); Yaniv Gurwicz (Intel Labs); Peer Nowack (Grantham Institute, Imperial College London. Department of Physics, Imperial College. Data Science Institute, Imperial College. School of Environmental Sciences, University of East Anglia); David Rolnick (McGill University, Mila)

Climate Science & Modeling Causal & Bayesian Methods
NeurIPS 2023 ClimateX: Do LLMs Accurately Assess Human Expert Confidence in Climate Statements? (Papers Track)
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Abstract: Evaluating the accuracy of outputs generated by Large Language Models (LLMs) is especially important in the climate science and policy domain. We introduce the Expert Confidence in Climate Statements (ClimateX) dataset, a novel, curated, expert-labeled dataset consisting of 8094 climate statements collected from the latest Intergovernmental Panel on Climate Change (IPCC) reports, labeled with their associated confidence levels. Using this dataset, we show that recent LLMs can classify human expert confidence in climate-related statements, especially in a few-shot learning setting, but with limited (up to 47%) accuracy. Overall, models exhibit consistent and significant over-confidence on low and medium confidence statements. We highlight implications of our results for climate communication, LLMs evaluation strategies, and the use of LLMs in information retrieval systems.

Authors: Romain Lacombe (Stanford University); Kerrie Wu (Stanford University); Eddie Dilworth (Stanford University)

Natural Language Processing Public Policy
NeurIPS 2023 Improving Flood Insights: Diffusion-based SAR to EO Image Translation (Papers Track)
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Abstract: Driven by the climate crisis, the frequency and intensity of flood events are on the rise. Electro-optical (EO) satellite imagery is commonly used for rapid disaster response. However, its utility in flood situations is limited by cloud cover and during nighttime. An alternative method for flood detection involves using Synthetic Aperture Radar (SAR) data. Despite SAR's advantages over EO in these situations, it has a significant drawback: human analysts often struggle to interpret SAR data. This paper proposes a novel framework, Diffusion-based SAR-to-EO Image Translation (DSE). The DSE framework converts SAR images into EO-like imagery, thereby enhancing their interpretability for human analysis. Experimental results on the Sen1Floods11 and SEN12-FLOOD datasets confirm that the DSE framework provides enhanced visual information and improves performance in all flood segmentation tests.

Authors: Minseok Seo (si-analytics); YoungTack Oh (SI Analytics); Doyi Kim (SI Analytics); Dongmin Kang (SIA); Yeji Choi (SI Analytics)

Disaster Management and Relief Earth Observation & Monitoring
NeurIPS 2023 A Wildfire Vulnerability Index for Businesses Using Machine Learning Approaches (Papers Track)
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Abstract: Climate change and housing growth in ecologically vulnerable areas are increasing the frequency and intensity of wildfire events. These events can have profound impact on communities and the economy, but the financial and operational impacts of wildfire on businesses have not been evaluated extensively yet. This paper presents a Wildfire Vulnerability Index (WVI) that measures the risk of a business failing in the 12 months following a wildfire event. An XGBoost algorithm champion model is compared to two challenger models: 1) a model that extends the champion model by incorporating building and property characteristics and 2) a model that utilizes a neural network approach. Results show that while all models perform well in predicting business failure risk post-disaster event, the model that uses building and property characteristics performs best across all performance metrics. As the natural environment shifts under climate change and more businesses are exposed to the consequences of wildfire, the WVI can help emergency managers allocate disaster aid to businesses at the highest risk of failing and can also provide valuable risk insights for portfolio managers and loan processors.

Authors: Andrew Byrnes (Dun and Bradstreet); Lisa Stites (Dun and Bradstreet)

Disaster Management and Relief
NeurIPS 2023 Exploring Causal Relationship between Environment and Drizzle Properties using Machine Learning (Papers Track)
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Abstract: Cloud and precipitation properties are controlled by both local and large-scale forcings. Current weather and climate models represent clouds and precipitation through parameterizations that are based on theoretical relationships between environment, clouds, and precipitation. However, these relationships vary considerably among different weather and cloud conditions, thereby leading to inaccurate simulation of cloud and precipitation properties. In this study, we use observations from a site in the Eastern North Atlantic Ocean (28W, 39.5N) to establish a potential causal relationship between large-scale environment, cloud, and precipitation properties. We estimate the structure of a directed acyclic graph (DAG) with the NOTEARS algorithm (Non-combinatorial Optimization via Trace Exponential and Augmented lagRangian for Structure learning) (Zheng et al., 2018 \cite{Zheng2018DAGsLearning}) with a multi-layer perceptron (MLP) neural network classification architecture. We classify liquid water path (LWP), rain rate, and rain drop diameter in two classes based on lower and upper quantiles to identify the governing mechanisms responsible for the two tails of the distribution. We also invoke Random Forest classification to compare our causal model results with conventional decision tree-based approaches. We hypothesize the dominant role of cloud LWP and net radiative cooling in controlling the cloud and precipitation properties. In this way, this study demonstrates the application of a causal machine learning method to identify which environmental properties potentially control cloud and precipitation development. These results will be extremely valuable to both observational and numerical modeling communities as they could help improve the current parameterizations in the weather and climate models.

Authors: Piyush Garg (Argonne National Laboratory); Virendra Ghate (Argonne National Laboratory); Maria Cadeddu (Argonne National Laboratory); Bethany Lusch (Argonne National Lab)

Climate Science & Modeling Causal & Bayesian Methods
NeurIPS 2023 Asset Bundling for Wind Power Forecasting (Papers Track)
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Abstract: The growing penetration of intermittent, renewable generation in US power grids results in increased operational uncertainty. In that context, accurate forecasts are critical, especially for wind generation, which exhibits large variability and is historically harder to predict. To overcome this challenge, this work proposes a novel Bundle-Predict-Reconcile (BPR) framework that integrates asset bundling, machine learning, and forecast reconciliation techniques to accurately predict wind power at the asset, bundle, and fleet level. Notably, our approach effectively introduces an auxiliary learning task (predicting the bundle-level time series) to help the main learning tasks (fleet-level time series) and proposes new asset-bundling criteria to capture the spatio-temporal dynamics of wind power time series. Extensive numerical experiments are conducted on an industry-size dataset of wind farms, demonstrating the benefits of BPR, which consistently and significantly improves forecast accuracy over the baseline approach, especially at the fleet level.

Authors: Hanyu Zhang (Georgia Institute of Technology); Mathieu Tanneau (Georgia Institute of Technology); Chaofan Huang (Georgia Institute of Technology); Roshan Joseph (Georgia Institute of Technology); Shangkun Wang (Georgia Institute of Technology); Pascal Van Hentenryck (Georgia Institute of Technology)

Power & Energy Time-series Analysis
NeurIPS 2023 Soil Organic Carbon Estimation from Climate-related Features with Graph Neural Network (Papers Track)
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Abstract: Soil organic carbon (SOC) plays a pivotal role in the global carbon cycle, impacting climate dynamics and necessitating accurate estimation for sustainable land and agricultural management. While traditional methods of SOC estimation face resolution and accuracy challenges, recent advancements harness remote sensing, machine learning, and high-resolution satellite mapping. Graph Neural Networks (GNNs), especially when integrated with positional encoders, offer promise in capturing intricate relationships between soil and climate. Using the LUCAS database, this study compared four GNN operators in the positional encoder framework. Results revealed that the PESAGE and PETransformer models outperformed others in SOC estimation, indicating their potential in capturing the complex interplay between SOC and climate features. Our findings underscore the potential of GNN architectures in advancing SOC prediction, paving the way for future explorations with more advanced GNN models.

Authors: Weiying Zhao (Deep Planet); Natalia Efremova (Queen Mary University London)

Carbon Capture & Sequestration Computer Vision & Remote Sensing
NeurIPS 2023 Deep Glacier Image Velocimetry: Mapping glacier velocities from Sentinel-2 imagery with deep learning (Papers Track)
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Abstract: Glacier systems are highly sensitive to climate change and play a pivotal role in global mean sea level rise. As such, it is important to monitor how glacier velocities and ice dynamics evolve under a changing climate. The growing wealth of satellite observations has facilitated the inference of glacier velocities from remote sensing imagery through feature tracking algorithms. At present, these rely on sparse cross-correlation estimates as well as computationally expensive optical flow solutions. Here we present a novel use of deep-learning for estimating annual glacier velocities, utilizing the recurrent optical-flow based architecture, RAFT, on consecutive pairs of optical Sentinel-2 imagery. Our results highlight that deep learning can generate dense per-pixel velocity estimates within an automated framework that utilizes Sentinel-2 images over the French Alps.

Authors: James B Tlhomole (Imperial College London); Matthew Piggott (Imperial College London); Graham Hughes (Imperial College London)

Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023 DeepEn2023: Energy Datasets for Edge Artificial Intelligence (Papers Track)
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Abstract: Climate change poses one of the most significant challenges to humanity. As a result of these climatic shifts, the frequency of weather, climate, and water-related disasters has multiplied fivefold over the past 50 years, resulting in over 2$ million deaths and losses exceeding U.S. $3.64 trillion. Leveraging AI-powered technologies for sustainable development and combating climate change is a promising avenue. Numerous significant publications are dedicated to using AI to improve renewable energy forecasting, enhance waste management, and monitor environmental changes in real-time. However, very few research studies focus on making AI itself environmentally sustainable. This oversight regarding the sustainability of AI within the field might be attributed to a mindset gap and the absence of comprehensive energy datasets. In addition, with the ubiquity of edge AI systems and applications, especially on-device learning, there is a pressing need to measure, analyze, and optimize their environmental sustainability, such as energy efficiency. To this end, in this paper, we propose large-scale energy datasets for edge AI, named DeepEn2023, covering a wide range of kernels, state-of-the-art deep neural network models, and popular edge AI applications. We anticipate that DeepEn2023 will enhance transparency regarding sustainability in on-device deep learning across a range of edge AI systems and applications.

Authors: XIAOLONG TU (Georgia State University); Anik Mallik (University of North Carolina at Charlotte); Jiang Xie (University of North Carolina at Charlotte); Haoxin Wang (Georgia State University)

NeurIPS 2023 Interpretable machine learning approach to understand U.S. prevented planting events and project climate change impacts (Papers Track)
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Abstract: Extreme weather events in 2019 prevented U.S. farmers from planting crop on a record 19.4 million acres, more than double the previous record. Insurance reports show the majority of these were intended to be corn acres and prevented due to excess soil moisture and precipitation. However, we still lack a detailed understanding of how weather and soil conditions lead to prevented planting, as well as how climate change may impact future outcomes. Machine learning provides a promising approach to this challenge. Here, we model the drivers of prevented corn planting using soil characteristics, monthly weather conditions, and geospatial information. Due to the extreme nature of events causing prevented planting, we use a novel-design zero-inflated regression (ZIR) model that predicts the occurrence of prevented planting as well as the potential severity. We identify key environmental drivers of prevented planting, including May rainfall and soil drainage class. Under climate change scenarios, the model interestingly projects future instances of prevented planting to be less frequent but more severe relative to the historical period.

Authors: Haynes Stephens (University of Chicago)

Agriculture & Food
NeurIPS 2023 GraphTransformers for Geospatial Forecasting of Hurricane Trajectories (Papers Track)
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Abstract: In this paper we introduce a novel framework for trajectory prediction of geospatial sequences using GraphTransformers. When viewed across several sequences, we observed that a graph structure automatically emerges between different geospatial points that is often not taken into account for such sequence modeling tasks. We show that by leveraging this graph structure explicitly, geospatial trajectory prediction can be significantly improved. Our GraphTransformer approach improves upon state-of-the-art Transformer based baseline significantly on HURDAT, a dataset where we are interested in predicting the trajectory of a hurricane on a 6 hourly basis. This helps inform evacuation efforts by narrowing down target location by 10 to 20 kilometers along both the north-south and east-west directions.

Authors: Satyaki Chakraborty (Carnegie Mellon University); Pallavi Banerjee (University of Washington)

Disaster Management and Relief Climate Science & Modeling
NeurIPS 2023 Integrating Building Survey Data with Geospatial Data: A Cluster-Based Ethical Approach (Papers Track)
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Abstract: This research paper delves into the unique energy challenges faced by Alaska, arising from its remote geographical location, severe climatic conditions, and heavy reliance on fossil fuels while emphasizing the shortage of comprehensive building energy data. The study introduces an ethical framework that leverages machine learning and geospatial techniques to enable the large-scale integration of data, facilitating the mapping of energy consumption data at the individual building level. Utilizing the Alaska Retrofit Information System (ARIS) and the USA Structures datasets, this framework not only identifies and acknowledges limitations inherent in existing datasets but also establishes a robust ethical foundation for data integration. This framework innovation sets a noteworthy precedent for the responsible utilization of data in the domain of climate justice research, ultimately informing the development of sustainable energy policies through an enhanced understanding of building data and advancing ongoing research agendas. Future research directions involve the incorporation of recently released datasets, which provide precise building location data, thereby further validating the proposed ethical framework and advancing efforts in addressing Alaska's intricate energy challenges.

Authors: Vidisha Chowdhury (University of Pennsylvania); Gabriela Gongora-Svartzman (Carnegie Mellon University); Erin D Trochim (University of Alaska Fairbanks); Philippe Schicker (Carnegie Mellon University)

Climate Justice Interpretable ML
NeurIPS 2023 Breeding Programs Optimization with Reinforcement Learning (Papers Track)
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Abstract: Crop breeding is crucial in improving agricultural productivity while potentially decreasing land usage, greenhouse gas emissions, and water consumption. However, breeding programs are challenging due to long turnover times, high-dimensional decision spaces, long-term objectives, and the need to adapt to rapid climate change. This paper introduces the use of Reinforcement Learning (RL) to optimize simulated crop breeding programs. RL agents are trained to make optimal crop selection and cross-breeding decisions based on genetic information. To benchmark RL-based breeding algorithms, we introduce a suite of Gym environments. The study demonstrates the superiority of RL techniques over standard practices in terms of genetic gain when simulated in silico using real-world genomic maize data.

Authors: Omar G. Younis (ETH Zurich); Luca Corinzia (ETH Zurich - Information Science & Engineering Group); Ioannis N Athanasiadis (Wageningen University and Research); Andreas Krause (ETH Zürich); Joachim Buhmann (ETH Zurich); Matteo Turchetta (ETH Zurich)

Reinforcement Learning Agriculture & Food
NeurIPS 2023 Discovering Effective Policies for Land-Use Planning (Papers Track) Best Pathway to Impact
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Abstract: How areas of land are allocated for different uses, such as forests, urban, and agriculture, has a large effect on carbon balance, and therefore climate change. Based on available historical data on changes in land use and a simulation of carbon emissions/absorption, a surrogate model can be learned that makes it possible to evaluate the different options available to decision-makers efficiently. An evolutionary search process can then be used to discover effective land-use policies for specific locations. Such a system was built on the Project Resilience platform and evaluated with the Land-Use Harmonization dataset and the BLUE simulator. It generates Pareto fronts that trade off carbon impact and amount of change customized to different locations, thus providing a potentially useful tool for land-use planning.

Authors: Risto Miikkulainen (UT Austin; Cognizant Technology Solutions); Olivier Francon (Cognizant AI Labs); Daniel Young (Cognizant AI Labs); Babak Hodjat (Cognizant AI Labs); Hugo Cunha (Cognizant AI Labs); Jacob Bieker (Open Climate Fix)

Forests
NeurIPS 2023 Probabilistic land cover modeling via deep autoregressive models (Papers Track)
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Abstract: Land use and land cover (LULC) modeling is a challenging task due to long-range dependencies between geographic features and distinct spatial patterns related in topography, ecology, and human development. We explore the usage of a modified Pixel Constrained CNN as applied to inpainting for categorical image data from the National Land Cover Database for producing a diverse set of land use counterfactual scenarios. We find that this approach is effective for producing a distribution of realistic image completions in certain masking configurations. However, the resulting distribution is not well-calibrated in terms of spatial summary statistics commonly used with LULC data and exhibits substantial underdispersion.

Authors: Christopher Krapu (Duke University); Ryan Calder (Virginia Tech); Mark Borsuk (Duke University)

Forests Uncertainty Quantification & Robustness
NeurIPS 2023 Simulating the Air Quality Impact of Prescribed Fires Using a Graph Neural Network-Based PM2.5 Emissions Forecasting System (Papers Track)
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Abstract: The increasing size and severity of wildfires across western North America have generated dangerous levels of PM2.5 pollution in recent years. In a warming climate, expanding the use of prescribed fires is widely considered to be the most robust fire mitigation strategy. However, reliably forecasting the potential air quality impact from these prescribed fires, a critical ingredient in determining the fires’ location and time, at hourly to daily time scales remains a challenging problem. This paper proposes a novel integration of prescribed fire simulation with a spatio-temporal graph neural network-based PM2.5 forecasting model. The experiments in this work focus on determining the optimal time for implementing prescribed fires in California as well as quantifying the potential air quality trade-offs involved in conducting more prescribed fires outside the fire season.

Authors: Kyleen Liao (Saratoga High School); Jatan Buch (Columbia University); Kara D. Lamb (Columbia University); Pierre Gentine (Columbia University)

Earth Observation & Monitoring Generative Modeling
NeurIPS 2023 Hyperspectral shadow removal with iterative logistic regression and latent Parametric Linear Combination of Gaussians (Papers Track)
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Abstract: Shadow detection and removal is a challenging problem in the analysis of hyperspectral images. Yet, this step is crucial for analyzing data for remote sensing applications like methane detection. In this work, we develop a shadow detection and removal method only based on the spectrum of each pixel and the overall distribution of spectral values. We first introduce Iterative Logistic Regression(ILR) to learn a spectral basis in which shadows can be linearly classified. We then model the joint distribution of the mean radiance and the projection coefficients of the spectra onto the above basis as a parametric linear combination of Gaussians. We can then extract the maximum likelihood mixing parameter of the Gaussians to estimate the shadow coverage and to correct the shadowed spectra. Our correction scheme reduces correction artefacts at shadow borders. The shadow detection and removal method is applied to hyperspectral images from MethaneAIR, a precursor to the satellite MethaneSAT.

Authors: Core Francisco Park (Harvard University); Maya Nasr (Harvard University); Manuel Pérez-Carrasco (University of Concepcion); Eleanor Walker (Harvard University); Douglas Finkbeiner (Harvard University); Cecilia Garraffo (AstroAI at the Center for Astrophysics, Harvard & Smitnsonian)

Earth Observation & Monitoring Uncertainty Quantification & Robustness
NeurIPS 2023 Cooperative Logistics: Can Artificial Intelligence Enable Trustworthy Cooperation at Scale? (Papers Track)
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Abstract: Cooperative Logistics studies the setting where logistics companies pool their resources together to improve their individual performance. Prior literature suggests carbon savings of approximately 22%. If attained globally, this equates to 480,000,000 tonnes of CO2-eq. Whilst well-studied in operations research – industrial adoption remains limited due to a lack of trustworthy cooperation. A key remaining challenge is fair and scalable gain sharing (i.e., how much should each company be fairly paid?). We propose the use of deep reinforcement learning with a neural reward model for coalition structure generation and present early findings.

Authors: Stephen Mak (University of Cambridge); Tim Pearce (Microsoft Research); Matthew Macfarlane (University of Amsterdam); Liming Xu (University of Cambridge); Michael Ostroumov (Value Chain Lab); Alexandra Brintrup (University of Cambridge)

Transportation Reinforcement Learning
NeurIPS 2023 Resource Efficient and Generalizable Representation Learning of High-Dimensional Weather and Climate Data (Papers Track)
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Abstract: We study self-supervised representation learning on high-dimensional data under resource constraints. Our work is motivated by applications of vision transformers to weather and climate data. Such data frequently comes in the form of tensors that are both higher dimensional and of larger size than the RGB imagery one encounters in many computer vision experiments. This raises scaling issues and brings up the need to leverage available compute resources efficiently. Motivated by results on masked autoencoders, we show that it is possible to use sampling of subtensors as the sole augmentation strategy for contrastive learning with a sampling ratio of $\sim$1\%. This is to be compared to typical masking ratios of $75\%$ or $90\%$ for image and video data respectively. In an ablation study, we explore extreme sampling ratios and find comparable skill for ratios as low as $\sim$0.0625\%. Pursuing efficiencies, we are finally investigating whether it is possible to generate robust embeddings on dimension values which were not present at training time. We answer this question to the positive by using learnable position encoders which have continuous dependence on dimension values.

Authors: Juan Nathaniel (Columbia University); Marcus Freitag (IBM); Patrick Curran (Environment and Climate Change Canada); Isabel Ruddick (Environment and Climate Change Canada); Johannes Schmude (IBM)

Unsupervised & Semi-Supervised Learning Meta- and Transfer Learning
NeurIPS 2023 Difference Learning for Air Quality Forecasting Transport Emulation (Papers Track)
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Abstract: Human health is negatively impacted by poor air quality including increased risk for respiratory and cardiovascular disease. Due to a recent increase in extreme air quality events, both globally and locally in the United States, finer resolution air quality forecasting guidance is needed to effectively adapt to these events. The National Oceanic and Atmospheric Administration provides air quality forecasting guidance for the Continental United States. Their air quality forecasting model is based on a 15 km spatial resolution; however, the goal is to reach a three km spatial resolution. This is currently not feasible due in part to prohibitive computational requirements for modeling the transport of chemical species. In this work, we describe a deep learning transport emulator that is able to reduce computations while maintaining skill comparable with the existing numerical model. We show how this method maintains skill in the presence of extreme air quality events, making it a potential candidate for operational use. We also explore evaluating how well this model maintains the physical properties of the modeled transport for a given set of species.

Authors: Reed R Chen (Johns Hopkins University Applied Physics Laboratory); Christopher Ribaudo (Johns Hopkins University Applied Physics Laboratory); Jennifer Sleeman (University of Maryland, Baltimore County and Johns Hopkins University Applied Physics Laboratory); Chace Ashcraft (JHU/APL); Marisa Hughes (JHU)

Extreme Weather
NeurIPS 2023 Fusion of Physics-Based Wildfire Spread Models with Satellite Data using Generative Algorithms (Papers Track)
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Abstract: Climate change has driven increases in wildfire prevalence, prompting development of wildfire spread models. Advancements in the use of satellites to detect fire locations provides opportunity to enhance fire spread forecasts from numerical models via data assimilation. In this work, a method is developed to infer the history of a wildfire from satellite measurements using a conditional Wasserstein Generative Adversarial Network (cWGAN), providing the information necessary to initialize coupled atmosphere-wildfire models in a physics-informed approach based on measurements. The cWGAN, trained with solutions from WRF-SFIRE, produces samples of fire arrival times (fire history) from the conditional distribution of arrival times given satellite measurements, and allows for assessment of prediction uncertainty. The method is tested on four California wildfires and predictions are compared against measured fire perimeters and reported ignition times. An average Sorensen's coefficient of 0.81 for the fire perimeters and an average ignition time error of 32 minutes suggests that the method is highly accurate.

Authors: Bryan Shaddy (University of Southern California); Deep Ray (University of Maryland); Angel Farguell (San Jose State University); Valentina Calaza (University of Southern California); Jan Mandel (University of Colorado Denver); James Haley (Cooperative Institute for Research in the Atmosphere); Kyle Hilburn (Cooperative Institute for Research in the Atmosphere); Derek Mallia (University of Utah); Adam Kochanski (San Jose State University); Assad Oberai (University of Southern California)

Generative Modeling Extreme Weather
NeurIPS 2023 A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network (Papers Track)
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Abstract: To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source. However, the complexity of computation in resolving high-resolution wind fields left the simulations impractical to test different time lengths and model configurations. This study presents a preliminary development of a physics-informed super-resolution (SR) generative adversarial network (GAN) that super-resolves the three-dimensional (3D) low-resolution wind fields by upscaling x9 times. We develop a pixel-wise self-attention (PWA) module that learns 3D weather dynamics via a self-attention computation followed by a 2D convolution. We also employ a loss term that regularizes the self-attention map during pretraining, capturing the vertical convection process from input wind data. The new PWA SR-GAN shows the high-fidelity super-resolved 3D wind data, learns a wind structure at the high-frequency domain, and reduces the computational cost of a high-resolution wind simulation by x 89.7 times.

Authors: Takuya Kurihana (University of Chicago); Levente Klein (IBM Research); Kyongmin Yeo (IBM Research); Daniela Szwarcman (IBM Research); Bruce G Elmegreen (IBM Research); Surya Karthik Mukkavilli (IBM Research, Zurich); Johannes Schmude (IBM)

Generative Modeling Climate Science & Modeling
NeurIPS 2023 Proof-of-concept: Using ChatGPT to Translate and Modernize an Earth System Model from Fortran to Python/JAX (Papers Track)
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Abstract: Earth system models (ESMs) are vital for understanding past, present, and future climate, but they suffer from legacy technical infrastructure. ESMs are primarily implemented in Fortran, a language that poses a high barrier of entry for early career scientists and lacks a GPU runtime, which has become essential for continued advancement as GPU power increases and CPU scaling slows. Fortran also lacks differentiability — the capacity to differentiate through numerical code — which enables hybrid models that integrate machine learning methods. Converting an ESM from Fortran to Python/JAX could resolve these issues. This work presents a semi-automated method for translating individual model components from Fortran to Python/JAX using a large language model (GPT-4). By translating the photosynthesis model from the Community Earth System Model (CESM), we demonstrate that the Python/JAX version results in up to 100x faster runtimes using GPU parallelization, and enables parameter estimation via automatic differentiation. The Python code is also easy to read and run and could be used by instructors in the classroom. This work illustrates a path towards the ultimate goal of making climate models fast, inclusive, and differentiable.

Authors: Anthony Zhou (Columbia University), Linnia Hawkins (Columbia University), Pierre Gentine (Columbia University)

Climate Science & Modeling Natural Language Processing
NeurIPS 2023 Learning to forecast diagnostic parameters using pre-trained weather embedding (Papers Track)
Abstract and authors: (click to expand)

Abstract: Data-driven weather prediction (DDWP) models are increasingly becoming popular for weather forecasting. However, while operational weather forecasts predict a wide variety of weather variables, DDWPs currently forecast a specific set of key prognostic variables. Non-prognostic ("diagnostic") variables are sometimes modeled separately as dependent variables of the prognostic variables (c.f. FourCastNet \cite{pathak2022fourcastnet}), or by including the diagnostic variable as a target in the DDWP. However, the cost of training and deploying bespoke models for each diagnostic variable can increase dramatically with more diagnostic variables, and limit the operational use of such models. Likewise, retraining an entire DDWP each time a new diagnostic variable is added is also cost-prohibitive. We present an two-stage approach that allows new diagnostic variables to be added to an end-to-end DDWP model without the expensive retraining. In the first stage, we train an autoencoder that learns to embed prognostic variables into a latent space. In the second stage, the autoencoder is frozen and "downstream" models are trained to predict diagnostic variables using only the latent representations of prognostic variables as input. Our experiments indicate that models trained using the two-stage approach offer accuracy comparable to training bespoke models, while leading to significant reduction in resource utilization during training and inference. This approach allows for new "downstream" models to be developed as needed, without affecting existing models and thus reducing the friction in operationalizing new models.

Authors: Peetak Mitra (Excarta); Vivek Ramavajjala (Excarta)

Climate Science & Modeling Hybrid Physical Models
NeurIPS 2023 PressureML: Modelling Pressure Waves to Generate Large-Scale Water-Usage Insights in Buildings (Papers Track)
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Abstract: Several studies have indicated that delivering insights and feedback on water usage has been effective in curbing water consumption, making it a pivotal component in achieving long-term sustainability objectives. Despite a significant proportion of water consumption originating from large residential and commercial buildings, there is a scarcity of cost-effective and easy-to-integrate solutions that provide water usage insights in such structures. Furthermore, existing methods for disaggregating water usage necessitate training data and rely on frequent data sampling to capture patterns, both of which pose challenges when scaling up and adapting to new environments. In this work, we aim to solve these challenges through a novel end-to-end approach which records data from pressure sensors and uses time-series classification by DNN models to determine room-wise water consumption in a building. This consumption data is then fed to a novel water disaggregation algorithm which can suggest a set of water-usage events, and has a flexible requirement of training data and sampling granularity. We conduct experiments using our approach and demonstrate its potential as a promising avenue for in-depth exploration, offering valuable insights into water usage on a large scale.

Authors: Tanmaey Gupta (Microsoft Research India); Anupam Sobti (IIT Delhi); Akshay Nambi (Microsoft Research)

Buildings Meta- and Transfer Learning
NeurIPS 2023 Facilitating Battery Swapping Services for Freight Trucks with Spatial-Temporal Demand Prediction (Papers Track)
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Abstract: Electrifying heavy-duty trucks offers a substantial opportunity to curtail carbon emissions, advancing toward a carbon-neutral future. However, the inherent challenges of limited battery energy and the sheer weight of heavy-duty trucks lead to reduced mileage and prolonged charging durations. Consequently, battery-swapping services emerge as an attractive solution for these trucks. This paper employs a two-fold approach to investigate the potential and enhance the efficacy of such services. Firstly, spatial-temporal demand prediction models are adopted to predict the traffic patterns for the upcoming hours. Subsequently, the prediction guides an optimization module for efficient battery allocation and deployment. Analyzing the heavy-duty truck data on a highway network spanning over 2,500 miles, our model and analysis underscore the value of prediction/machine learning in facilitating future decision-makings. In particular, we find that the initial phase of implementing battery-swapping services favors mobile battery-swapping stations, but as the system matures, fixed-location stations are preferred.

Authors: Linyu Liu (Tsinghua University); Zhen Dai (Chongqing Expressway Group Company); Shiji Song (Department of Automation, Tsinghua University); Xiaocheng Li (Imperial College London); Guanting Chen (The University of North Carolina at Chapel Hill)

Transportation
NeurIPS 2023 Domain Adaptation for Sustainable Soil Management using Causal and Contrastive Constraint Minimization (Papers Track)
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Abstract: Monitoring organic matter is pivotal for maintaining soil health and can help inform sustainable soil management practices. While sensor-based soil information offers higher-fidelity and reliable insights into organic matter changes, sampling and measuring sensor data is cost-prohibitive. We propose a multi-modal, scalable framework that can estimate organic matter from remote sensing data, a more readily available data source while leveraging sparse soil information for improving generalization. Using the sensor data, we preserve underlying causal relations among sensor attributes and organic matter. Simultaneously we leverage inherent structure in the data and train the model to discriminate among domains using contrastive learning. This causal and contrastive constraint minimization ensures improved generalization and adaptation to other domains. We also shed light on the interpretability of the framework by identifying attributes that are important for improving generalization. Identifying these key soil attributes that affect organic matter will aid in efforts to standardize data collection efforts.

Authors: Somya Sharma (U. Minnesota); Swati Sharma (Microsoft Research); RAFAEL PADILHA (Microsoft Research); Emre Kiciman (Microsoft Research); Ranveer Chandra (Microsoft Research)

Carbon Capture & Sequestration Causal & Bayesian Methods
NeurIPS 2023 Inference of CO2 flow patterns--a feasibility study (Papers Track)
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Abstract: As the global deployment of carbon capture and sequestration (CCS) technology intensifies in the fight against climate change, it becomes increasingly imperative to establish robust monitoring and detection mechanisms for potential underground CO2 leakage, particularly through pre-existing or induced faults in the storage reservoir's seals. While techniques such as history matching and time-lapse seismic monitoring of CO2 storage have been used successfully in tracking the evolution of CO2 plumes in the subsurface, these methods lack principled approaches to characterize uncertainties related to the CO2 plumes' behavior. Inclusion of systematic assessment of uncertainties is essential for risk mitigation for the following reasons: (i) CO2 plume-induced changes are small and seismic data is noisy; (ii) changes between regular and irregular (e.g., caused by leakage) flow patterns are small; and (iii) the reservoir properties that control the flow are strongly heterogeneous and typically only available as distributions. To arrive at a formulation capable of inferring flow patterns for regular and irregular flow from well and seismic data, the performance of conditional normalizing flow will be analyzed on a series of carefully designed numerical experiments. While the inferences presented are preliminary in the context of an early CO2 leakage detection system, the results do indicate that inferences with conditional normalizing flows can produce high-fidelity estimates for CO2 plumes with or without leakage. We are also confident that the inferred uncertainty is reasonable because it correlates well with the observed errors. This uncertainty stems from noise in the seismic data and from the lack of precise knowledge of the reservoir's fluid flow properties.

Authors: Abhinav Prakash Gahlot (Georgia Institute of Technology); Huseyin Tuna Erdinc (Georgia Institute of Technology); Rafael Orozco (Georgia Institute of Technology); Ziyi Yin (Georgia Institute of Technology); Felix Herrmann (Georgia Institute of Technology)

Carbon Capture & Sequestration Generative Modeling
NeurIPS 2023 Generative Nowcasting of Marine Fog Visibility in the Grand Banks area and Sable Island in Canada (Papers Track)
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Abstract: This study presents the application of generative deep learning techniques to evaluate marine fog visibility nowcasting using the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign observations collected during July 2022 in the North Atlantic in the Grand Banks area and vicinity of Sable Island (SI), northeast of Canada. The measurements were collected using the Vaisala Forward Scatter Sensor model FD70 and Weather Transmitter model WXT50, and Gill R3A ultrasonic anemometer mounted on the Research Vessel Atlantic Condor. To perform nowcasting, the time series of fog visibility (Vis), wind speed, dew point depression, and relative humidity with respect to water were preprocessed to have lagged time step features. Generative nowcasting of Vis time series for lead times of 30 and 60 minutes were performed using conditional generative adversarial networks (cGAN) regression at visibility thresholds of Vis < 1 km and < 10 km. Extreme gradient boosting (XGBoost) was used as a baseline method for comparison against cGAN. At the 30 min lead time, Vis was best predicted with cGAN at Vis < 1 km (RMSE = 0.151 km) and with XGBoost at Vis < 10 km (RMSE = 2.821 km). At the 60 min lead time, Vis was best predicted with XGBoost at Vis < 1 km (RMSE = 0.167 km) and Vis < 10 km (RMSE = 3.508 km), but the cGAN RMSE was similar to XGBoost. Despite nowcasting Vis at 30 min being quite difficult, the ability of the cGAN model to track the variation in Vis at 1 km suggests that there is potential for generative analysis of marine fog visibility using observational meteorological parameters.

Authors: Eren Gultepe (Southern Illinois University Edwardsville); Sen Wang (University of Notre Dame); Byron Blomquist (NOAA); Harindra Fernando (University of Notre Dame); Patrick Kreidl (University of North Florida); David Delene (University of North Dakota); Ismail Gultepe (Ontario Tech University)

Climate Science & Modeling Generative Modeling
NeurIPS 2023 Data-Driven Traffic Reconstruction and Kernel Methods for Identifying Stop-and-Go Congestion (Papers Track)
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Abstract: Identifying stop-and-go events (SAGs) in traffic flow presents an important avenue for advancing data-driven research for climate change mitigation and sustainability, owing to their substantial impact on carbon emissions, travel time, fuel consumption, and roadway safety. In fact, SAGs are estimated to account for 33-50\% of highway driving externalities. However, insufficient attention has been paid to precisely quantifying where, when, and how much these SAGs take place– necessary for downstream decision making, such as intervention design and policy analysis. A key challenge is that the data available to researchers and governments are typically sparse and aggregated to a granularity that obscures SAGs. To overcome such data limitations, this study thus explores the use of traffic reconstruction techniques for SAG identification. In particular, we introduce a kernel-based method for identifying spatio-temporal features in traffic and leverage bootstrapping to quantify the uncertainty of the reconstruction process. Experimental results on California highway data demonstrate the promise of the method for capturing SAGs. This work contributes to a foundation for data-driven decision making to advance sustainability of traffic systems.

Authors: Edgar Ramirez Sanchez (MIT); Shreyaa Raghavan (MIT); Cathy Wu ()

Transportation Uncertainty Quantification & Robustness
NeurIPS 2023 AI assisted Search for Atmospheric CO2 Capture (Papers Track)
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Abstract: Carbon capture technologies is an important tool for mitigating climate change. In recent years, polymer membrane separation methods have emerged as a promising technology for separating CO2 and other green house gases from the atmosphere. Designing new polymers for such tasks is quite difficult. In this work we look at machine learning based methods to search for new polymer designs optimized for CO2 separation. An ensemble ML models is trained on a large database of molecules to predict permeabilities of CO2/N2 and CO2/O2 pairs. We then use search based optimization to discover new polymers that surpass existing polymer designs. Simulations are then done to verify the predicted performance of the new designs. Overall result suggests that ML based search can be used to discover new polymers optimized for carbon capture.

Authors: Shivashankar Shivashankar (Student)

Carbon Capture & Sequestration Reinforcement Learning
NeurIPS 2023 Towards Recommendations for Value Sensitive Sustainable Consumption (Papers Track)
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Abstract: Excessive consumption can strain natural resources, harm the environment, and widen societal gaps. While adopting a more sustainable lifestyle means making significant changes and potentially compromising personal desires, balancing sustainability with personal values poses a complex challenge. This article delves into designing recommender systems using neural networks and genetic algorithms, aiming to assist consumers in shopping sustainably without disregarding their individual preferences. We approach the search for good recommendations as a problem involving multiple objectives, representing diverse sustainability goals and personal values. While using a synthetic historical dataset based on real-world sources, our evaluations reveal substantial environmental benefits without demanding drastic personal sacrifices, even if consumers accept only a fraction of the recommendations.

Authors: Thomas Asikis (University of Zurich)

Recommender Systems Societal Adaptation & Resilience
NeurIPS 2023 Towards autonomous large-scale monitoring the health of urban trees using mobile sensing (Papers Track)
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Abstract: Healthy urban greenery is a fundamental asset to mitigate climate change phenomenons such as extreme heat and air pollution. However, urban trees are often affected by abiotic and biotic stressors that hamper their functionality, and whenever not timely managed, even their survival. The current visual or instrumented inspection techniques often require a high amount of human labor making frequent assessments infeasible at a city-wide scale. In this work, we present the GreenScan Project, a ground-based sensing system designed to provide health assessment of urban trees at high space-time resolutions, with low costs. The system utilises thermal and multi-spectral imaging sensors, fused using computer vision models to estimate two tree health indexes, namely NDVI and CTD. Preliminary evaluation of the system was performed through data collection experiments in Cambridge, USA. Overall, this work illustrates the potential of autonomous mobile ground-based tree health monitoring on city-wide scales at high temporal resolutions with low-costs.

Authors: Akshit Gupta (Delft University of Technology); Martine Rutten (Delft University of Technology); RANGA RAO VENKATESHA PRASAD (TUDelft); Remko Uijlenhoet (Delft University of Technology)

Cities & Urban Planning Computer Vision & Remote Sensing
NeurIPS 2023 Towards Global, General-Purpose Pretrained Geographic Location Encoders (Papers Track)
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Abstract: Location information is essential for modeling tasks in climate-related fields ranging from ecology to the Earth system sciences. However, obtaining meaningful location representation is challenging and requires a model to distill semantic location information from available data, such as remote sensing imagery. To address this challenge, we introduce SatCLIP, a global, general-purpose geographic location encoder that provides vector embeddings summarizing the characteristics of a given location for convenient usage in diverse downstream tasks. We show that SatCLIP embeddings, pretrained on multi-spectral Sentinel-2 satellite data, can be used for various predictive out-of-domain tasks, including temperature prediction and animal recognition in imagery, and outperform existing competing approaches. SatCLIP embeddings also prove helpful in overcoming geographic domain shift. This demonstrates the potential of general-purpose location encoders and opens the door to learning meaningful representations of our planet from the vast, varied, and largely untapped modalities of geospatial data.

Authors: Konstantin Klemmer (Microsoft Research); ESTHER ROLF (Google Research); Caleb Robinson (Microsoft AI for Good Research Lab); Lester Mackey (Microsoft Research); Marc Rußwurm (École Polytechnique Fédérale de Lausanne)

Unsupervised & Semi-Supervised Learning Computer Vision & Remote Sensing
NeurIPS 2023 Spatially Regularized Graph Attention Autoencoder Framework for Detecting Rainfall Extremes (Papers Track)
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Abstract: We introduce a novel Graph Attention Autoencoder (GAE) with spatial regularization to address the challenge of scalable anomaly detection in spatiotemporal rainfall data across India from 1990 to 2015. Our model leverages a Graph Attention Network (GAT) to capture spatial dependencies and temporal dynamics in the data, further enhanced by a spatial regularization term ensuring geographic coherence. We construct two graph datasets employing rainfall, pressure, and temperature attributes from the Indian Meteorological Department and ERA5 Reanalysis on Single Levels, respectively. Our network operates on graph representations of the data, where nodes represent geographic locations, and edges, inferred through event synchronization, denote significant co-occurrences of rainfall events. Through extensive experiments, we demonstrate that our GAE effectively identifies anomalous rainfall patterns across the Indian landscape. Our work paves the way for sophisticated spatiotemporal anomaly detection methodologies in climate science, contributing to better climate change preparedness and response strategies.

Authors: Mihir Agarwal (IIT Gandhinagar); Progyan Das (IIT Gandhinagar); Udit Bhatia (IIT Gandhinagar)

Climate Science & Modeling
NeurIPS 2023 Antarctic Bed Topography Super-Resolution via Transfer Learning (Papers Track)
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Abstract: High-fidelity topography models of the bedrock underneath the thick Antarctic ice sheet can improve scientists' understanding of ice flow and its contributions to sea level rise. However, the bed topography of Antarctica is one of the most challenging surfaces on earth to map, requiring aircrafts with ice penetrating radars to survey the vast and remote continent. We propose FROST, Fusion Regression for Optimal Subglacial Topography, a method that leverages readily available surface topography data from satellites as an auxiliary input modality for bed topography super-resolution. FROST uses a non-parametric Gaussian Process model to transfer local, non-stationary covariance patterns from the ice surface to the bedrock. In a controlled topography reconstruction experiment over complex East Antarctic terrain, our proposed method outperforms bicubic interpolation at all five tested magnification factors, reducing RMSE by 67% at x2, and 25% at x6 magnification. This work demonstrates the opportunity for data fusion methods to advance downstream climate modelling and steward climate change adaptation.

Authors: Kim Bente (The University of Sydney); Roman Marchant (University of Technology Sydney); Fabio Ramos (NVIDIA, The University of Sydney)

Computer Vision & Remote Sensing Climate Science & Modeling
NeurIPS 2023 Elucidating the Relationship Between Climate Change and Poverty using Graph Neural Networks, Ensemble Models, and Remote Sensing Data (Papers Track)
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Abstract: Climate and poverty are intrinsically related: regions with extreme temperatures, large temperature variability, and recurring extreme weather events tend to be ranked among the poorest and most vulnerable to climate change. Nevertheless, there currently is no established method to directly estimate the impact of specific climate variables on poverty and to identify geographical regions at high risk of being negatively affected by climate change. In this work, we propose a new approach based on Graph Neural Networks (GNNs) to estimate the effect of climate and remote sensing variables on poverty indicators measuring Education, Health, Living Standards, and Income. Furthermore, we use the trained models and perturbation analyses to identify the geographical regions most vulnerable to the potential variations in climate variables.

Authors: Parinthapat Pengpun (Bangkok Christian International School); Alessandro Salatiello (University of Tuebingen)

Climate Finance & Economics Earth Observation & Monitoring
NeurIPS 2023 Sustainability AI copilot: Analyze & ideate at scale to enable positive impact (Papers Track)
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Abstract: With the advances in large scale Foundation Models, web scale access to sustainability data, planetary scale satellite data, the opportunity for larger section of the world population to create positive climate impact can be activated by empowering everyone to ideate via AI copilots. The challenge is: How to enable more people to think & take action on climate & Sustainable Development goals?. We develop AI co-pilots to engage broader community for enabling impact at scale by democratizing climate thinking & ideation tools. We demonstrated how ideating with SAI transforms any seed idea into a holistic one, given the relation between climate & social economic aspects. SAI employs Language Models to represent the voice of the often neglected vulnerable people to the brainstorming discussion for inclusive climate action. We demonstrated how SAI can even create another AI that learns geospatial insights and offers advice to prevent humanitarian disasters from climate change. In this work, we conceptualized, designed, implemented & demonstrated Sustainability AI copilot (SAI) & innovated 4 use cases:- SAI enables sustainability enthusiasts to convert early stage budding thoughts into a robust holistic idea by creatively employing a chain of Large Language Models to think with six-thinking hats ideation. SAI can enables non-experts to become geospatial analysts by generating code to analyze planetary scale satellite data. SAI also ideates in multi-modal latent space to explore climate friendly product designs. SAI also enables human right activists to create awareness about inclusion of vulnerable and persons with disability in the climate conversation. SAI even creates AI apps for persons with disability. We demonstrated working prototypes at the project website, https://sites.google.com/view/climate-copilot . Thus, SAI co-pilot empowers everyone to come together to ideate to make progress on climate and related sustainable development goals.

Authors: Rajagopal A (Indian Institute of Technology); Nirmala V (Queen Marys); Immanuel Raja (Karunya University); Arun V (NIT)

Earth Observation & Monitoring Generative Modeling
NeurIPS 2023 Causality and Explainability for Trustworthy Integrated Pest Management (Proposals Track)
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Abstract: Pesticides, widely used in agriculture for pest control, contribute to the climate crisis. Integrated pest management (IPM) is preferred as a climate-smart alternative. However, low adoption rates of IPM are observed due to farmers' skepticism about its effectiveness, so we introduce an enhancing data analysis framework for IPM to combat that. Our framework provides i) robust pest population predictions across diverse environments with invariant and causal learning, ii) interpretable pest presence predictions using transparent models, iii) actionable advice through counterfactual explanations for in-season IPM interventions, iv) field-specific treatment effect estimations, and v) causal inference to assess advice effectiveness.

Authors: Ilias Tsoumas (National Observatory of Athens); Vasileios Sitokonstantinou (University of Valencia); Georgios Giannarakis (National Observatory of Athens); Evagelia Lampiri (University of Thessaly); Christos Athanassiou (University of Thessaly); Gustau Camps-Valls (Universitat de València); Charalampos Kontoes (National Observatory of Athens); Ioannis N Athanasiadis (Wageningen University and Research)

Causal & Bayesian Methods Agriculture & Food
NeurIPS 2023 Monitoring Sustainable Global Development Along Shared Socioeconomic Pathways (Proposals Track)
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Abstract: Sustainable global development is one of the most prevalent challenges facing the world today, hinging on the equilibrium between socioeconomic growth and environmental sustainability. We propose approaches to monitor and quantify sustainable development along the Shared Socioeconomic Pathways (SSPs), including mathematically derived scoring algorithms, and machine learning methods. These integrate socioeconomic and environmental datasets, to produce an interpretable metric for SSP alignment. An initial study demonstrates promising results, laying the groundwork for the application of different methods to the monitoring of sustainable global development.

Authors: Michelle Wan (University of Cambridge); Jeff Clark (University of Bristol); Edward Small (Royal Melbourne Institute of Technology); Elena Fillola (University of Bristol); Raul Santos Rodriguez (University of Bristol)

Public Policy Time-series Analysis
NeurIPS 2023 Understanding Climate Legislation Decisions with Machine Learning (Proposals Track)
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Abstract: Effective action is crucial in order to avert climate disaster. Key in enacting change is the swift adoption of climate positive legislation which advocates for climate change mitigation and adaptation. This is because government legislation can result in far-reaching impact, due to the relationships between climate policy, technology, and market forces. To advocate for legislation, current strategies aim to identify potential levers and obstacles, presenting an opportunity for the application of recent advances in machine learning language models. Here we propose a machine learning pipeline to analyse climate legislation, aiming to investigate the feasibility of natural language processing for the classification of climate legislation texts, to predict policy voting outcomes. By providing a model of the decision making process, the proposed pipeline can enhance transparency and aid policy advocates and decision makers in understanding legislative decisions, thereby providing a tool to monitor and understand legislative decisions towards climate positive impact.

Authors: Jeff Clark (University of Bristol); Michelle Wan (University of Cambridge); Raul Santos Rodriguez (University of Bristol)

Public Policy Natural Language Processing
NeurIPS 2023 Mapping the Landscape of Artificial Intelligence in Climate Change Research: A Meta-Analysis on Impact and Applications (Proposals Track)
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Abstract: This proposal advocates a comprehensive and systematic analysis aimed at mapping and characterizing the intricate landscape of Artificial Intelligence and Machine Learning applications and their impacts within the domain of climate change research, both in adaption and mitigation efforts. Notably, a significant upswing in this interdisciplinary intersection has been observed since 2020. Utilizing advanced topic clustering techniques and qualitative analysis, we have discerned 12 distinct macro areas that supplement, enrich, and expand upon those identified in prior research. The primary objective of this undertaking is to furnish a data-rich panoramic view and informative insights regarding the functions and tools of the mentioned disciplines. Our intention is to offer valuable guidance to the scholarly community and propel further research endeavors, encouraging meticulous examinations of research trends and gaps in addressing the formidable challenges posed by climate change and the climate crisis.

Authors: Christian Burmester (Osnabrück University); Teresa Scantamburlo (UniversityofVenice)

Natural Language Processing Behavioral and Social Science
NeurIPS 2023 Unlocking the Potential of Renewable Energy Through Curtailment Prediction (Proposals Track)
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Abstract: A significant fraction (5-15%) of renewable energy generated goes into waste in the grids around the world today due to oversupply issues and transmission constraints. Being able to predict when and where renewable curtailment occurs would improve renewable utilization. The core of this work is to enable the machine learning community to help decarbonize electricity grids by unlocking the potential of renewable energy through curtailment prediction.

Authors: Bilge Acun (Meta / FAIR); Brent Morgan (Meta); Henry Richardson (WattTime); Nat Steinsultz (WattTime); Carole-Jean Wu (Meta / FAIR)

Power & Energy
NeurIPS 2023 Assessing data limitations in ML-based LCLU (Proposals Track)
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Abstract: This study addresses the accuracy challenge in Global Land Use and Land Cover (LULC) maps, crucial for policy making towards climate change mitigation. We evaluate two LULC products based on advanced machine learning techniques across two representative nations, Ecuador and Germany, employing a novel accuracy metric. The analysis unveils a notable accuracy enhancement in the convolutional neural network-based approach against the random forest model used for comparison. Our findings emphasize the potential of sophisticated machine learning methodologies in advancing LULC mapping accuracy, an essential stride towards data-driven, climate-relevant land management and policy decisions.

Authors: Angel Encalada-Davila (ESPOL); Christian Tutiven (ESPOL University); Jose E Cordova-Garcia (ESPOL)

Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2023 Predicting Adsorption Energies for Catalyst Screening with Transfer Learning Using Crystal Hamiltonian Graph Neural Network (Proposals Track)
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Abstract: As the world moves towards a clean energy future to mitigate the risks of climate change, the discovery of new catalyst materials plays a significant role in enabling the sustainable production and transformation of energy [2]. The development and verification of fast, accurate, and efficient artificial intelligence and machine learning techniques is critical to shortening time-intensive calculations, reducing costs, and improving computational feasibility. We propose applying the Crystal Hamiltonian Graph Neural Network (CHGNet) on the OC20 dataset in order to iteratively perform structure-to-energy and forces calculations and identify the lowest energy across relaxed structures for a given adsorbate-surface combination. CHGNet's predictions will be compared and benchmarked to corresponding values calculated by density functional theory (DFT) [7] and other models to determine its efficacy.

Authors: Angelina Chen (Foothill College/Lawrence Berkeley National Lab); Hui Zheng (Lawrence Berkeley National Lab); Paula Harder (Mila)

Meta- and Transfer Learning Chemistry & Materials
NeurIPS 2023 Physics-informed DeepONet for battery state prediction (Proposals Track)
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Abstract: Electrification has emerged as a pivotal trend in the energy transition to address climate change, leading to a substantial surge in the demand for batteries. Accurately predicting the internal states and performance of batteries assumes paramount significance, as it ensures the safe and stable operation of batteries and informs decision-making processes, such as optimizing battery operation for arbitrage opportunities. However, current models struggle to strike a balance between precision and computational efficiency or are limited in their applicability to specific scenarios. We aim to adopt a physics-informed deep operator network (PI-DeepONet) for internal battery state estimation based on the rigorous P2D model, which can simultaneously achieve high precision and computational efficiency. Furthermore, it exhibits promising prospects for extension beyond lithium-ion batteries to encompass various battery technologies.

Authors: Keyan Guo (Peking University)

Power & Energy Hybrid Physical Models
NeurIPS 2023 Decarbonizing Maritime Operations: A Data-Driven Revolution (Proposals Track)
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Abstract: The maritime industry faces an unprecedented challenge in the form of decarbonization. With strict emissions reduction targets in place, the industry is turning to machine learning-based decision support models to achieve sustainability goals. This proposal explores the transformative potential of digitalization and machine learning approaches in maritime operations, from optimizing ship speeds to enhancing supply chain management. By examining various machine learning techniques, this work provides a roadmap for reducing emissions while improving operational efficiency in the maritime sector.

Authors: Ismail Bourzak (UQAR); Loubna Benabou (UQAR); Sara El Mekkaoui (DNV); Abdelaziz Berrado (EMI Engineering School)

Oceans & Marine Systems
NeurIPS 2023 High-resolution Global Building Emissions Estimation using Satellite Imagery (Proposals Track)
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Abstract: Globally, buildings account for 30% of end-use energy consumption and 27% of energy sector emissions, and yet the building sector is lacking in low-temporal-latency, high-spatial-resolution data on energy consumption and resulting emissions. Existing methods tend to either have low resolution, high latency (often a year or more), or rely on data typically unavailable at scale (such as self-reported energy consumption). We propose a machine learning based bottom-up model that combines satellite-imagery-derived features to compute Scope 1 global emissions estimates both for residential and commercial buildings at a 1 square km resolution with monthly global updates.

Authors: Paul J Markakis (Duke University); Jordan Malof (University of Montana); Trey Gowdy (Duke University); Leslie Collins (Duke University); Aaron Davitt (WattTime); Gabriela Volpato (WattTime); Kyle Bradbury (Duke University)

Buildings Computer Vision & Remote Sensing
NeurIPS 2023 Sand Mining Watch: Leveraging Earth Observation Foundation Models to Inform Sustainable Development (Proposals Track)
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Abstract: As the major ingredient of concrete and asphalt, sand is vital to economic growth, and will play a key role in aiding the transition to a low carbon society. However, excessive and unregulated sand mining in the Global South has high socio-economic and environmental costs, and amplifies the effects of climate change. Sand mines are characterized by informality and high temporal variability, and data on the location and extent of these mines tends to be sparse. We propose to build custom sand-mine detection tools by fine-tuning foundation models for earth observation, which leverage self supervised learning - a cost-effective and powerful approach in sparse data regimes. Our preliminary results show that these methods outperform fully supervised approaches, with the best performing model achieving an average precision score of 0.57 for this challenging task. These tools allow for real-time monitoring of sand mining activity and can enable more effective policy and regulation, to inform sustainable development.

Authors: Ando Shah (UC Berkeley); Suraj R Nair (UC Berkeley); Tom Boehnel (TU Munich); Joshua Blumenstock (University of California, Berkeley)

Earth Observation & Monitoring Unsupervised & Semi-Supervised Learning
NeurIPS 2023 Understanding Insect Range Shifts with Out-of-Distribution Detection (Proposals Track)
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Abstract: Climate change is inducing significant range shifts in insects and other organisms. Large-scale temporal data on populations and distributions are essential for quantifying the effects of climate change on biodiversity and ecosystem services, providing valuable insights for both conservation and pest management. With images from camera traps, we aim to use Mahalanobis distance-based confidence scores to automatically detect new moth species in a region. We intend to make out-of-distribution detection interpretable by identifying morphological characteristics of different species using Grad-CAM. We hope this algorithm will be a useful tool for entomologists to study range shifts and inform climate change adaptation.

Authors: Yuyan Chen (McGill University, Mila); David Rolnick (McGill University, Mila)

Ecosystems & Biodiversity Computer Vision & Remote Sensing
NeurIPS 2023 Aquaculture Mapping: Detecting and Classifying Aquaculture Ponds using Deep Learning (Tutorials Track)
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Abstract: Mapping aquaculture ponds is critical for restoration, conservation, and climate adaptation efforts. Aquaculture can contribute to high levels of water pollution from untreated effluent and negatively impact coastal ecosystems. Large-scale aquaculture is also a significant driver in mangrove deforestation, thus reducing the world’s carbon sinks and exacerbating the effects of climate change. However, finding and mapping these ponds on the ground can be highly labor and time-intensive. Most existing automated techniques are focused only on spatial location and do not consider production intensification, which is also crucial to understanding their impact on the surrounding ecosystem. We can classify them into two main types: a) Extensive ponds, which are large, irregularly-shaped ponds that rely on natural productivity, and b) intensive ponds which are smaller and regularly shaped. Intensive ponds use machinery such as aerators that maximize production and also result in the characteristic presence of air bubbles on the pond’s surface. The features of these two types of ponds make them distinguishable and detectable from satellite imagery. In this tutorial, we will discuss types of aquaculture ponds in detail and demonstrate how they can be detected and classified using satellite imagery. The tutorial will introduce an open dataset of human-labeled aquaculture ponds in the Philippines and Indonesia. Using this dataset, the tutorial will use semantic segmentation to map out similar ponds over an entire country and classify them as either extensive or intensive, going through the entire process of i) satellite imagery retrieval, ii) preprocessing these images into a training-ready dataset, iii) model training, and iv) finally model rollout on a sample area. Throughout, the tutorial will leverage PyTorch Lightning, a machine learning framework that provides a simplified and streamlined interface for model experimentation and deployment. This tutorial aims to discuss the relevance of aquaculture ponds in climate adaptation and equip users with the necessary inputs and tools to perform their own ML-powered earth observation projects.

Authors: John Christian G Nacpil (Thinking Machines Data Science, Inc.); Joshua Cortez (Thinking Machines Data Science)

Computer Vision & Remote Sensing Earth Observation & Monitoring
NeurIPS 2023 Zero-Emission Vehicle Intelligence (ZEVi): Effectively Charging Electric Vehicles at Scale Without Breaking Power Systems (or the Bank) (Tutorials Track)
Abstract and authors: (click to expand)

Abstract: Transportation contributes to 29% of all greenhouse gas (GHG) emissions in the US, of which 58% are from light-duty vehicles and 28% from medium-to-heavy duty vehicles (MHDVs) [1]. Battery electric vehicles (EVs) emit 90% less life cycle GHGs than their internal combustion engine (ICEV) counterparts [2], but currently only comprise 2% of all vehicles in the U.S. EVs thus represent a crucial step in decarbonizing road transportation. One major challenge in replacing ICEVs with EVs at scale is the ability to charge a large number of EVs within the constraints of power systems in a cost-effective way. This is an especially prominent problem for MHDVs used in commercial fleets such as shuttle buses and delivery trucks, as they generally require more energy to complete assigned trips compared to light-duty vehicles. In this tutorial, we describe the myriad challenges in charging EVs at scale and define common objectives such as minimizing total load on power systems, minimizing fleet operating costs, as well as maximizing vehicle state of charge and onsite photovoltaic energy usage. We discuss common constraints such as vehicle trip energy requirements, charging station power limits, and limits on vehicles’ time to charge between trips. We survey several different methods to formulate EV charging and energy dispatch as a mathematically solvable optimization problem, using tools such as convex optimization, Markov decision process (MDP), and reinforcement learning (RL). We introduce a commercial application of model-based predictive control (MPC) algorithm, ZEVi (Zero Emission Vehicle intelligence), which solves optimal energy dispatch strategies for charging sessions of commercial EV fleets. Using a synthetic dataset modeled after a real fleet of electric school buses, we engage the audience with a hands-on exercise applying ZEVi to find the optimal charging strategy for a commercial fleet. Lastly, we briefly discuss other contexts in which methods originating from process control and deep learning, like MPC and RL, can be applied to solve problems related to climate change mitigation and adaptation. With the examples provided in this tutorial, we hope to inspire the audience to come up with their own creative ways to apply these methods in different fields within the climate domain. References [1] EPA (2023). Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2021. U.S. Environmental Protection Agency, EPA 430-R-23-002. [2] Verma, S., Dwivedi, G., & Verma, P. (2022). Life cycle assessment of electric vehicles in comparison to combustion engine vehicles: A review. Materials Today: Proceedings, 49, 217-222.

Authors: Shasha Lin (NextEra Mobility); Jonathan Brophy (NextEra Mobility); Tamara Monge (NextEra Mobility); Jamie Hussman (NextEra Mobility); Michelle Lee (NextEra Mobility); Sam Penrose (NextEra Mobility)

Transportation Reinforcement Learning
NeurIPS 2023 Agile Modeling for Bioacoustic Monitoring (Tutorials Track)
Abstract and authors: (click to expand)

Abstract: Bird, insect, and other wild animal populations are rapidly declining, highlighting the need for better monitoring, understanding, and protection of Earth’s remaining wild places. However, direct monitoring of biodiversity is difficult. Passive Acoustic Monitoring (PAM) enables detection of the vocalizing species in an ecosystem, many of which can be difficult or impossible to detect by satellite or camera trap. Large-scale PAM deployments using low-cost devices allow measuring changes over time and responses to environmental changes, and targeted deployments can discover and monitor endangered or invasive species. Machine learning methods are needed to analyze the thousands or even millions of hours of audio produced by large-scale deployments. But there are a massive number of potential signals to target for bioacoustic measurement, and many of the most interesting lack training data. Many rare species are difficult to observe. Detecting specific call-types and juvenile calls can give further insight into behavior and population health, but almost no structured datasets exist for these use-cases. No single classifier can address all of these needs, so practitioners regularly need to create new classifiers to address novel problems. Soundscape annotation efforts are very expensive, and machine learning experts are scarce, creating a bottleneck on analysis. We aim to eliminate the bottleneck by providing an efficient, self-contained active learning workflow for biologists. In this tutorial, we present an integrated workflow for analyzing large unlabeled bioacoustic datasets, adapting new agile modeling techniques to audio. Our goal is to allow experts to create a new high quality classifier for a novel class with under one hour of effort. We achieve this by leveraging transfer learning from high-quality bioacoustic models, vector search over audio databases, and lightweight Python notebook UX. The workflow can begin from a single example, proceeds through an efficient active learning loop, and finally applies the produced classifier to a large mass of unlabeled data to produce insights for ecologists and land managers.

Authors: tom denton (google); Jenny Hamer (Google Research); Rob Laber (Google)

Ecosystems & Biodiversity Active Learning
ICLR 2023 Tutorial: Quantus x Climate - Applying explainable AI evaluation in climate science (Tutorials Track)
Abstract and authors: (click to expand)

Abstract: Explainable artificial intelligence (XAI) methods shed light on the predictions of deep neural networks (DNNs). In the climate context, XAI has been applied to improve and validate deep learning (DL) methods while providing researchers with new insight into physical processes. However, the evaluation, validation and selection of XAI methods are challenging due to often lacking ground truth explanations. In this tutorial, we introduce the XAI evaluation package Quantus to the climate community. We start by providing the users with pre-processed input and output data alongside a convolutional neural network (CNN) trained to assign yearly temperature maps to classes according to their decade. We explain the network prediction of an example temperature map using five different explanation techniques Gradient GradientShap, IntegratedGradients, LRP-z and Occlusion. By visually analyzing each explanation method around the North Atlantic (NA) cooling patch 10-80W, 20-60N, we provide a motivating example that shows that different explanations may disagree in their explained evidence which subsequently can lead to different scientific interpretation and potentially, misleading conclusions. We continue by introducing Quantus including the explanation properties that can be evaluated such as robustness, faithfulness, complexity, localization and randomization. We guide the participants towards a practical understanding of XAI evaluation by demonstrating how metrics differ in their scoring and interpretation. Moreover, we teach the participants to compare and select an appropriate XAI method by performing a comprehensive XAI evaluation. Lastly, we return to the motivating example, highlighting how Quantus can facilitate well-founded XAI research in climate science.

Authors: Philine L Bommer (TU Berlin); Anna Hedström (Technische Universität Berlin); Marlene Kretschmer (University of Reading); Marina M.-C. Höhne (TU Berlin)

Interpretable ML Climate Science & Modeling
ICLR 2023 CityLearn: A Tutorial on Reinforcement Learning Control for Grid-Interactive Efficient Buildings and Communities (Tutorials Track)
Abstract and authors: (click to expand)

Abstract: Buildings are responsible for up to 75% of electricity consumption in the United States. Grid-Interactive Efficient Buildings can provide flexibility to solve the issue of power supply-demand mismatch, particularly brought about by renewables. Their high energy efficiency and self-generating capabilities can reduce demand without affecting the building function. Additionally, load shedding and shifting through smart control of storage systems can further flatten the load curve and reduce grid ramping cost in response to rapid decrease in renewable power supply. The model-free nature of reinforcement learning control makes it a promising approach for smart control in grid-interactive efficient buildings, as it can adapt to unique building needs and functions. However, a major challenge for the adoption of reinforcement learning in buildings is the ability to benchmark different control algorithms to accelerate their deployment on live systems. CityLearn is an open source OpenAI Gym environment for the implementation and benchmarking of simple and advanced control algorithms, e.g., rule-based control, model predictive control or deep reinforcement learning control thus, provides solutions to this challenge. This tutorial leverages CityLearn to demonstrate different control strategies in grid-interactive efficient buildings. Participants will learn how to design three controllers of varying complexity for battery management using a real-world residential neighborhood dataset to provide load shifting flexibility. The algorithms will be evaluated using six energy flexibility, environmental and economic key performance indicators, and their benefits and shortcomings will be identified. By the end of the tutorial, participants will acquire enough familiarity with the CityLearn environment for extended use in new datasets or personal projects.

Authors: Kingsley E Nweye (The University of Texas at Austin); Allen Wu (The University of Texas at Austin); Hyun Park (The University of Texas at Austin); Yara Almilaify (The University of Texas at Austin); Zoltan Nagy (The University of Texas at Austin)

Buildings Cities & Urban Planning Power & Energy Reinforcement Learning Time-series Analysis
ICLR 2023 Smart Meter Data Analytics: Practical Use-Cases and Best Practices of Machine Learning Applications for Energy Data in the Residential Sector (Tutorials Track)
Abstract and authors: (click to expand)

Abstract: To cope with climate change, the energy system is undergoing a massive transformation. With the electrification of all sectors, the power grid is facing high additional demand. As a result, the digitization of the grid is becoming more of a focus. The smart grid relies heavily on the increasing deployment of smart electricity meters around the world. The corresponding smart meter data is typically a time series of power or energy measurements with a resolution of 1s to 60 min. This data provides valuable insights and opportunities for monitoring and controlling activities in the power grid. In this tutorial, we therefore provide an overview of best practices for analyzing smart meter data. We focus on machine learning applications and low resolution (15-60 minutes) energy data in a residential setting. We only use real-world datasets and cover use-cases that are highly relevant for practical applications. Although this tutorial is specifically tailored to an audience from the energy domain, we believe that anyone from the data analytics and machine learning community can benefit from it, as many techniques are applicable to any time series data. Through our tutorial, we hope to foster new ideas, contribute to an interdisciplinary exchange between different research fields, and educate people about energy use.

Authors: Tobias Brudermueller (ETH Zurich); Markus Kreft (ETH Zurich)

Power & Energy Buildings Time-series Analysis
ICLR 2023 Mitigating climate and health impact of small-scale kiln industry using multi-spectral classifier and deep learning (Papers Track)
Abstract and authors: (click to expand)

Abstract: Industrial air pollution has a direct health impact and is a major contributor to climate change. Small scale industries particularly bull-trench brick kilns are one of the major causes of air pollution in South Asia often creating hazardous levels of smog that is injurious to human health. To mitigate the climate and health impact of the kiln industry, fine-grained kiln localization at different geographic locations is needed. Kiln localization using multi-spectral remote sensing data such as vegetation index results in a noisy estimates whereas use of high-resolution imagery is infeasible due to cost and compute complexities. This paper proposes a fusion of spatio-temporal multi-spectral data with high-resolution imagery for detection of brick kilns within the "Brick-Kiln-Belt" of South Asia. We first perform classification using low-resolution spatio-temporal multi-spectral data from Sentinel-2 imagery by combining vegetation, burn, build up and moisture indices. Then orientation aware object detector: YOLOv3 (with theta value) is implemented for removal of false detections and fine-grained localization. Our proposed technique, when compared with other benchmarks, results in a 21 times improvement in speed with comparable or higher accuracy when tested over multiple countries.

Authors: Usman Nazir (Lahore University of Management Sciences); Murtaza Taj (Lahore University of Management Sciences); Momin Uppal (Lahore University of Management Sciences); Sara khalid (University of Oxford)

Earth Observation & Monitoring Health
ICLR 2023 Global Flood Prediction: a Multimodal Machine Learning Approach (Papers Track)
Abstract and authors: (click to expand)

Abstract: Flooding is one of the most destructive and costly natural disasters, and climate changes would further increase risks globally. This work presents a novel mul- timodal machine learning approach for multi-year global flood risk prediction, combining geographical information and historical natural disaster dataset. Our multimodal framework employs state-of-the-art processing techniques to extract embeddings from each data modality, including text-based geographical data and tabular-based time-series data. Experiments demonstrate that a multimodal ap- proach, that is combining text and statistical data, outperforms a single-modality approach. Our most advanced architecture, employing embeddings extracted us- ing transfer learning upon DistilBert model, achieves 75%-77% ROCAUC score in predicting the next 1-5 year flooding event in historically flooded locations. This work demonstrates the potentials of using machine learning for long-term planning in natural disaster management

Authors: Cynthia Zeng (MIT); Dimitris Bertsimas (MIT)

Extreme Weather Disaster Management and Relief
ICLR 2023 Improving global high-resolution Earth system model simulations of precipitation with generative adversarial networks (Papers Track)
Abstract and authors: (click to expand)

Abstract: Precipitation extremes are expected to become stronger and more frequent in response to anthropogenic global warming. Accurately projecting the ecological and socioeconomic impacts is an urgent task. Impact models are developed and calibrated with observation-based data but rely on Earth system model (ESM) output for future scenarios. ESMs, however, exhibit significant biases in their output because they cannot fully resolve complex cross-scale interactions of processes that produce precipitation cannot. State-of-the-art bias correction methods only address errors in the simulated frequency distributions, locally at every individual grid cell. Improving unrealistic spatial patterns of the ESM output, which would require spatial context, has not been possible so far. Here we show that a post-processing method based on physically constrained generative adversarial networks (GANs) can correct biases of a state-of-the-art global ESM both in local frequency distributions and in the spatial patterns at once. While our method improves local frequency distributions similarly well to a gold-standard ESM bias-adjustment framework, it strongly outperforms existing methods in correcting spatial patterns. Our study highlights the importance of physical constraints in neural networks for out-of-sample predictions in the context of climate change.

Authors: Philipp Hess (Technical University of Munich)

Generative Modeling Climate Science & Modeling Extreme Weather Computer Vision & Remote Sensing Hybrid Physical Models
ICLR 2023 Machine Learning for Advanced Building Construction (Papers Track)
Abstract and authors: (click to expand)

Abstract: High-efficiency retrofits can play a key role in reducing carbon emissions associated with buildings if processes can be scaled-up to reduce cost, time, and disruption. Here we demonstrate an artificial intelligence/computer vision (AI/CV)-enabled framework for converting exterior build scans and dimensional data directly into manufacturing and installation specifications for overclad panels. In our workflow point clouds associated with LiDAR-scanned buildings are segmented into a facade feature space, vectorized features are extracted using an iterative random-sampling consensus algorithm, and from this representation an optimal panel design plan satisfying manufacturing constraints is generated. This system and the corresponding construction process is demonstrated on a test facade structure constructed at the National Renewable Energy Laboratory (NREL). We also include a brief summary of a techno-economic study designed to estimate the potential energy and cost impact of this new system.

Authors: Hilary Egan (NREL); Clement Fouquet (Trimble Inc.); Chioke Harris (NREL)

Buildings
ICLR 2023 Coregistration of Satellite Image Time Series Through Alignment of Road Networks (Papers Track)
Abstract and authors: (click to expand)

Abstract: Due to climate change, thawing permafrost affects transportation infrastructure in northern regions. Tracking deformations over time of these structures can allow identifying the most vulnerable sections to permafrost degradation and implement climate adaptation strategies. The Sentinel-2 mission provides data well-suited for multitemporal analysis due to its high temporal resolution and multispectral coverage. However, the geometrical misalignment of Sentinel-2 imagery makes this analysis challenging. Towards the goal of estimating the deformation of linear infrastructure in northern Canada, we propose an automatic subpixel coregistration algorithm for satellite image time series based on the matching of binary masks of roads produced by a deep learning model. We demonstrate the feasibility of achieving subpixel coregistration through alignment of roads on a small dataset of high-resolution Sentinel-2 images from the region of Gillam in northern Canada. This is the first step towards training a road deformation prediction model.

Authors: Andres Felipe Perez Murcia (University of Manitoba); Pooneh Maghoul (University of Manitoba); Ahmed Ashraf (University of Manitoba)

Earth Observation & Monitoring Transportation Computer Vision & Remote Sensing
ICLR 2023 Estimating Residential Solar Potential using Aerial Data (Papers Track)
Abstract and authors: (click to expand)

Abstract: Project Sunroof estimates the solar potential of residential buildings using high quality aerial data. That is, it estimates the potential solar energy (and associated financial savings) that can be captured by buildings if solar panels were to be installed on their roofs. Unfortunately its coverage is limited by the lack of high resolution digital surface map (DSM) data. We present a deep learning approach that bridges this gap by enhancing widely available low-resolution data, thereby dramatically increasing the coverage of Sunroof. We also present some ongoing efforts to potentially improve accuracy even further by replacing certain algorithmic components of Sunroof’s processing pipeline with deep learning.

Authors: Ross Goroshin (Google); Carl Elkin (Google)

Power & Energy Computer Vision & Remote Sensing
ICLR 2023 Improving extreme weather events detection with light-weight neural networks (Papers Track)
Abstract and authors: (click to expand)

Abstract: To advance automated detection of extreme weather events, which are increasing in frequency and intensity with climate change, we explore modifications to a novel light-weight Context Guided convolutional neural network architecture trained for semantic segmentation of tropical cyclones and atmospheric rivers in climate data. Our primary focus is on tropical cyclones, the most destructive weather events, for which current models show limited performance. We investigate feature engineering, data augmentation, learning rate modifications, alternative loss functions, and architectural changes. In contrast to previous approaches optimizing for intersection over union, we specifically seek to improve recall to penalize under-counting and prioritize identification of tropical cyclones. We report success through the use of weighted loss functions to counter class imbalance for these rare events. We conclude with directions for future research on extreme weather events detection, a crucial task for prediction, mitigation, and equitable adaptation to the impacts of climate change.

Authors: Romain Lacombe (Stanford University); Hannah Grossman (Stanford); Lucas P Hendren (Stanford University); David Ludeke (Stanford University)

Extreme Weather Climate Science & Modeling Computer Vision & Remote Sensing
ICLR 2023 CaML: Carbon Footprinting of Products with Zero-Shot Semantic Text Similarity (Papers Track)
Abstract and authors: (click to expand)

Abstract: Estimating the embodied carbon in products is a key step towards understanding their impact, and undertaking mitigation actions. Precise carbon attribution is challenging at scale, requiring both domain expertise and granular supply chain data. As a first-order approximation, standard reports use Economic Input-Output based Life Cycle Assessment (EIO-LCA) which estimates carbon emissions per dollar at an industry sector level using transactions between different parts of the economy. For EIO-LCA, an expert needs to map each product to one of upwards of 1000 potential industry sectors. We present CaML, an algorithm to automate EIO-LCA using semantic text similarity matching by leveraging the text descriptions of the product and the industry sector. CaML outperforms the previous manually intensive method, yielding a MAPE of 22% with no domain labels.

Authors: Bharathan Balaji (Amazon); Venkata Sai Gargeya Vunnava (amazon); Geoffrey Guest (Amazon); Jared Kramer (Amazon)

Natural Language Processing Supply Chains
ICLR 2023 Graph Neural Network Generated Metal-Organic Frameworks for Carbon Capture (Proposals Track)
Abstract and authors: (click to expand)

Abstract: The level of carbon dioxide (CO2) in our atmosphere is rapidly rising and is projected to double today‘s levels to reach 1,000 ppm by 2100 under certain scenarios, primarily driven by anthropogenic sources. Technology that can capture CO2 from anthropogenic sources, remove from atmosphere and sequester it at the gigaton scale by 2050 is required stop and reverse the impact of climate change. Metal-organic frameworks (MOFs) have been a promising technology in various applications including gas separation as well as CO2 capture from point-source flue gases or removal from the atmosphere. MOFs offer unmatched surface area through their highly porous crystalline structure and MOF technology has potential to become a leading adsorption-based CO2 separation technology providing high surface area, structure stability and chemical tunability. Due to its complex structure, MOF crystal structure (atoms and bonds) cannot be easily represented in tabular format for machine learning (ML) applications whereas graph neural networks (GNN) have already been explored in representation of simpler chemical molecules. In addition to difficulty in MOF data representation, an infinite number of combinations can be created for MOF crystals, which makes ML applications more suitable to alleviate dependency on subject matter experts (SME) than conventional computational methods. In this work, we propose training of GNNs in variational autoencoder (VAE) setting to create an end-to-end workflow for the generation of new MOF crystal structures directly from the data within the crystallographic information files (CIFs) and conditioned by additional CO2 performance values.

Authors: Zikri Bayraktar (Schlumberger Doll Research); Shahnawaz Molla (Schlumberger Doll Research); Sharath Mahavadi (Schlumberger Doll Research)

Generative Modeling Chemistry & Materials
ICLR 2023 Long-lead forecasts of wintertime air stagnation index in southern China using oceanic memory effects (Papers Track)
Abstract and authors: (click to expand)

Abstract: Stagnant weather condition is one of the major contributors to air pollution as it is favorable for the formation and accumulation of pollutants. To measure the atmosphere’s ability to dilute air pollutants, Air Stagnation Index (ASI) has been introduced as an important meteorological index. Therefore, making long-lead ASI forecasts is vital to make plans in advance for air quality management. In this study, we found that autumn Niño indices derived from sea surface temperature (SST) anomalies show a negative correlation with wintertime ASI in southern China, offering prospects for a prewinter forecast. We developed an LSTM-based model to predict the future wintertime ASI. Results demonstrated that multivariate inputs (past ASI and Niño indices) achieve better forecast performance than univariate input (only past ASI). The model achieves a correlation coefficient of 0.778 between the actual and predicted ASI, exhibiting a high degree of consistency.

Authors: Chenhong Zhou (Hong Kong Baptist University); Xiaorui Zhang (Hong Kong Baptist University); Meng Gao (Hong Kong Baptist University); Shanshan Liu (University of science and technology of China); Yike Guo (Hong Kong University of Science and Technology); Jie Chen (Hong Kong Baptist University)

Climate Science & Modeling Extreme Weather Time-series Analysis
ICLR 2023 Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions (Papers Track)
Abstract and authors: (click to expand)

Abstract: Streamflow forecasts are critical to guide water resource management, mitigate drought and flood effects, and develop climate-smart infrastructure and governance. Many global regions, however, have limited streamflow observations to guide evidence-based management strategies. In this paper, we propose an attention-based domain adaptation streamflow forecaster for data-sparse regions. Our approach leverages the hydrological characteristics of a data-rich source domain to induce effective 24hr lead-time streamflow prediction in a data-constrained target domain. Specifically, we employ a deep-learning framework leveraging domain adaptation techniques to simultaneously train streamflow predictions and discern between both domains using an adversarial method. Experiments against baseline cross-domain forecasting models show improved performance for 24hr lead-time streamflow forecasting.

Authors: Roland R Oruche (University of Missouri-Columbia); Fearghal O'Donncha (IBM Research)

Meta- and Transfer Learning Disaster Management and Relief Time-series Analysis
ICLR 2023 Predicting Cycling Traffic in Cities: Is bike-sharing data representative of the cycling volume? (Proposals Track)
Abstract and authors: (click to expand)

Abstract: A higher share of cycling in cities can lead to a reduction in greenhouse gas emissions, a decrease in noise pollution, and personal health benefits. Data-driven approaches to planning new infrastructure to promote cycling are rare, mainly because data on cycling volume are only available selectively. By leveraging new and more granular data sources, we predict bicycle count measurements in Berlin, using data from free-floating bike-sharing systems in addition to weather, vacation, infrastructure, and socioeconomic indicators. To reach a high prediction accuracy given the diverse data, we make use of machine learning techniques. Our goal is to ultimately predict traffic volume on all streets beyond those with counters and to understand the variance in feature importance across time and space. Results indicate that bike-sharing data are valuable to improve the predictive performance, especially in cases with high outliers, and help generalize the models to new locations.

Authors: Silke K. Kaiser (Hertie School)

Transportation
ICLR 2023 Unsupervised machine learning techniques for multi-model comparison: A case study on Antarctic Intermediate Water in CMIP6 models (Papers Track)
Abstract and authors: (click to expand)

Abstract: The Climate Model Intercomparison Project provides access to ensembles of model experiments that are widely used to better understand past, present, and future climate changes. In this study, we use Principal Component Analysis and K-means and hierarchical clustering techniques to guide identification of models in the CMIP6 dataset that are best suited for specific modelling objectives. An example is discussed here that focuses on how CMIP6 models reproduce the physical properties of Antarctic Intermediate Water, a key feature of the global oceanic circulation and of the ocean-climate system, noting that the tools and methods introduced here can readily be extended to the analysis of other features and regions.

Authors: Ophelie Meuriot (Imperial College London); Yves Plancherel (Imperial College London); Veronica Nieves (University of Valencia)

Climate Science & Modeling Unsupervised & Semi-Supervised Learning
ICLR 2023 An automatic mobile approach for Tree DBH Estimation Using a Depth Map and a Regression Convolutional Neural Network (Papers Track)
Abstract and authors: (click to expand)

Abstract: Carbon credit programs finance projects to reduce emissions, remove pollutants, improve livelihoods, and protect natural ecosystems. Ensuring the quality and integrity of such projects is essential to their success. One of the most important variables used in nature-based solutions to measure carbon sequestration is the diameter at breast height (DBH) of trees. In this paper, we propose an automatic mobile computer vision method to estimate the DBH of a tree using a single depth map on a smartphone, along with our created dataset DepthMapDBH2023. We successfully demonstrated that this dataset paired with a lightweight regression convolutional neural network is able to accurately estimate the DBH of trees distinct in appearance, shape, number of tree forks, tree density and crowding, and vine presence. Automation of these measurements will help crews in the field who are collecting data for forest inventories. Gathering as much on-the-ground data as possible is required to ensure the transparency of carbon credit projects. Access to high-quality datasets of manual measurements helps improve biomass models which are widely used in the field of ecological simulation. The code used in this paper will be publicly available on Github and the dataset on Kaggle.

Authors: Margaux Masson-Forsythe (Earthshot Labs); Margaux Masson-Forsythe (Earthshot Labs)

Computer Vision & Remote Sensing Carbon Capture & Sequestration Climate Science & Modeling Forests Land Use
ICLR 2023 Safe Multi-Agent Reinforcement Learning for Price-Based Demand Response (Papers Track)
Abstract and authors: (click to expand)

Abstract: Price-based demand response (DR) enables households to provide the flexibility required in power grids with a high share of volatile renewable energy sources. Multi-agent reinforcement learning (MARL) offers a powerful, decentralized decision-making tool for autonomous agents participating in DR programs. Unfortunately, MARL algorithms do not naturally allow one to incorporate safety guarantees, preventing their real-world deployment. To meet safety constraints, we propose a safety layer that minimally adjusts each agent's decisions. We investigate the influence of using a reward function that reflects these safety adjustments. Results show that considering safety aspects in the reward during training improves both convergence speed and performance of the MARL agents in the investigated numerical experiments.

Authors: Hannah Markgraf (Technical University of Munich); Matthias Althoff (Technical University of Munich)

Reinforcement Learning Buildings Power & Energy
ICLR 2023 BurnMD: A Fire Projection and Mitigation Modeling Dataset (Papers Track)
Abstract and authors: (click to expand)

Abstract: Today's fire projection modeling tools struggle to keep up with the rapid rate and increasing severity of climate change, leaving disaster managers dependent on tools which are increasingly unrepresentative of complex interactions between fire behavior, environmental conditions, and various mitigation options. This has consequences for equitably minimizing wildfire risks to life, property, ecology, cultural heritage, and public health. Fortunately, decades of data exist for fuel populations, weather conditions, and outcomes of significant fires in the American West and globally. The fire management community faces a lack of data standardization and validation among many competing fire models. Likewise, the machine learning community lacks curated datasets and benchmarks to develop solutions necessary to generate impact in this space. We present a novel dataset composed of 308 medium sized fires from the years 2018-2021, complete with both time series airborne based inference and ground operational estimation of fire extent, and operational mitigation data such as control line construction. As the first large wildfire dataset with mitigation information, Burn Mitigation Dataset (BurnMD) will help advance fire projection modeling, fire risk modeling, and AI generated land management policies.

Authors: Marissa Dotter (MITRE Corporation)

Forests Climate Science & Modeling Computer Vision & Remote Sensing Time-series Analysis
ICLR 2023 MAHTM: A Multi-Agent Framework for Hierarchical Transactive Microgrids (Papers Track)
Abstract and authors: (click to expand)

Abstract: Integration of variable renewable energy into the grid has posed challenges to system operators in achieving optimal trade-offs among energy availability, cost affordability, and pollution controllability. This paper proposes a multi-agent reinforcement learning framework for managing energy transactions in microgrids. The framework addresses the challenges above: it seeks to optimize the usage of available resources by minimizing the carbon footprint while benefiting all stakeholders. The proposed architecture consists of three layers of agents, each pursuing different objectives. The first layer, comprised of prosumers and consumers, minimizes the total energy cost. The other two layers control the energy price to decrease the carbon impact while balancing the consumption and production of both renewable and conventional energy. This framework also takes into account fluctuations in energy demand and supply.

Authors: Nicolas M Cuadrado (MBZUAI); Roberto Alejandro Gutierrez Guillen (MBZUAI); Yongli Zhu (Texas A&M University); Martin Takac (Mohamed bin Zayed University of Artificial Intelligence)

Power & Energy Reinforcement Learning
ICLR 2023 Improving a Shoreline Forecasting Model with Symbolic Regression (Papers Track)
Abstract and authors: (click to expand)

Abstract: Given the current context of climate change and the increasing population densities at coastal zones around the globe, there is an increasing need to be able to predict the development of our coasts. Recent advances in artificial intelligence allow for automatic analysis of observational data. Symbolic Regression (SR) is a type of Machine Learning algorithm that aims to find interpretable symbolic expressions that can explain relations in the data. In this work, we aim to study the problem of forecasting shoreline change using SR. We make use of Cartesian Genetic Programming (CGP) in order to encode and improve upon ShoreFor, a physical shoreline prediction model. During training, CGP individuals are evaluated and selected according to their predictive score at five different coastal sites. This work presents a comparison between a CGP-evolved model and the base ShoreFor model. In addition to evolution's ability to produce well-performing models, it demonstrates the usefulness of SR as a research tool to gain insight into the behaviors of shorelines in various geographical zones.

Authors: Mahmoud AL NAJAR (Laboratory of Spatial Geophysics and Oceanography Studies); Rafael ALMAR (Laboratory of Spatial Geophysics and Oceanography Studies); Erwin BERGSMA (CNES); Jean-Marc DELVIT (CNES); Dennis Wilson (ISAE)

Interpretable ML Earth Observation & Monitoring Extreme Weather Hybrid Physical Models Time-series Analysis
ICLR 2023 Remote Control: Debiasing Remote Sensing Predictions for Causal Inference (Papers Track)
Abstract and authors: (click to expand)

Abstract: Understanding and properly estimating the impacts of environmental interventions is of critical importance as we work towards achieving global climate goals. Remote sensing has become an essential tool for evaluating when and where climate policies have positive impacts on factors like greenhouse gas emissions and carbon sequestration. However, when machine learning models trained to predict outcomes using remotely sensed data simply minimize a standard loss function, the predictions that they generate can produce biased estimates in downstream causal inference. If prediction error in the outcome variable is correlated with policy variables or important confounders, as is the case for many widely used remote sensing data sets, estimates of the causal impacts of policies can be biased. In this paper, we demonstrate how this bias can arise, and we propose the use of an adversarial debiasing model (Zhang, Lemoine, and Mitchell 2018) in order to correct the issue when using satellite data to generate machine learning predictions for use in causal inference. We apply this method to a case study of the relationship between roads and tree cover in West Africa, where our results indicate that adversarial debiasing can recover a much more accurate estimate of the parameter of interest compared to when the standard approach is used.

Authors: Matthew Gordon (Yale); Megan Ayers (Yale University); Eliana Stone (Yale School of the Environment); Luke C Sanford (Yale School of the Environment)

Forests Computer Vision & Remote Sensing Land Use
ICLR 2023 A simplified machine learning based wildfire ignition model from insurance perspective (Papers Track)
Abstract and authors: (click to expand)

Abstract: In the context of climate change, wildfires are becoming more frequent, intense, and prolonged in the western US, particularly in California. Wildfires cause catastrophic socio-economic losses and are projected to worsen in the near future. Inaccurate estimates of fire risk put further pressure on wildfire (re)insurance and cause many homes to lose wildfire insurance coverage. Efficient and effective prediction of fire ignition is one step towards better fire risk assessment. Here we present a simplified machine learning-based fire ignition model at yearly scale that is well suited to the use case of one-year term wildfire (re)insurance. Our model yields a recall, precision, and the area under the precision-recall curve of 0.69, 0.86 and 0.81, respectively, for California, and significantly higher values of 0.82, 0.90 and 0.90, respectively, for the populated area, indicating its good performance. In addition, our model feature analysis reveals that power line density, enhanced vegetation index (EVI), vegetation optical depth (VOD), and distance to the wildland-urban interface stand out as the most important features determining ignitions. The framework of this simplified ignition model could easily be applied to other regions or genesis of other perils like hurricane, and it paves the road to a broader and more affordable safety net for homeowners.

Authors: Yaling Liu (OurKettle Inc); Son Le (OurKettle Inc.); Yufei Zou (Our Kettle, Inc.); mojtaba Sadgedhi (OurKettle Inc.); Yang Chen (University of California, Irvine); Niels Andela (BeZero Carbon); Pierre Gentine (Columbia University)

Earth Observation & Monitoring Climate Science & Modeling Societal Adaptation & Resilience Interpretable ML
ICLR 2023 Nested Fourier Neural Operator for Basin-Scale 4D CO2 Storage Modeling (Papers Track)
Abstract and authors: (click to expand)

Abstract: Carbon capture and storage (CCS) plays an essential role in global decarbonization. Scaling up CCS requires accurate and high-resolution modeling of the storage reservoir pressure buildup and the gaseous plume migration. However, such modeling is very challenging at scale due to the high computational costs of existing numerical methods. This challenge leads to significant uncertainty in evaluating storage opportunities which can delay the pace of global CCS deployments. We introduce a machine-learning approach for dynamic basin-scale modeling that speeds up flow prediction nearly 700,000 times compared to existing methods. Our framework, Nested Fourier Neural Operator (FNO), provides a general-purpose simulator alternative under diverse reservoir conditions, geological heterogeneity, and injection schemes. It enables unprecedented real-time high-fidelity modeling to support decision-making in basin-scale CCS projects.

Authors: Gege Wen (Stanford University)

Carbon Capture & Sequestration Climate Science & Modeling
ICLR 2023 SEA LEVEL PROJECTIONS WITH MACHINE LEARNING USING ALTIMETRY AND CLIMATE MODEL ENSEMBLES (Papers Track)
Abstract and authors: (click to expand)

Abstract: Satellite altimeter observations retrieved since 1993 show that the global mean sea level is rising at an unprecedented rate (3.4mm/year). With almost three decades of observations, we can now investigate the contributions of anthropogenic climate-change signals such as greenhouse gases, aerosols, and biomass burning in this rising sea level. We use machine learning (ML) to investigate future patterns of sea level change. To understand the extent of contributions from the climate-change signals, and to help in forecasting sea level change in the future, we turn to climate model simulations. This work presents a machine learning framework that exploits both satellite observations and climate model simulations to generate sea level rise projections at a 2-degree resolution spatial grid, 30 years into the future. We train fully connected neural networks (FCNNs) to predict altimeter values through a non-linear fusion of the climate model hindcasts (for 1993-2019). The learned FCNNs are then applied to future climate model projections to predict future sea level patterns. We propose segmenting our spatial dataset into meaningful clusters and show that clustering helps to improve predictions of our ML model.

Authors: Saumya Sinha (University of Colorado, Boulder); John Fasullo (NCAR); R. Steven Nerem (Univesity of Colorado, Boulder); Claire Monteleoni (University of Colorado Boulder)

Climate Science & Modeling Oceans & Marine Systems
ICLR 2023 Global-Local Policy Search and Its Application in Grid-Interactive Building Control (Papers Track)
Abstract and authors: (click to expand)

Abstract: As the buildings sector represents over 70% of the total U.S. electricity consumption, it offers a great amount of untapped demand-side resources to tackle many critical grid-side problems and improve the overall energy system's efficiency. To help make buildings grid-interactive, this paper proposes a global-local policy search method to train a reinforcement learning (RL) based controller which optimizes building operation during both normal hours and demand response (DR) events. Experiments on a simulated five-zone commercial building demonstrate that by adding a local fine-tuning stage to the evolution strategy policy training process, the control costs can be further reduced by 7.55% in unseen testing scenarios. Baseline comparison also indicates that the learned RL controller outperforms a pragmatic linear model predictive controller (MPC), while not requiring intensive online computation.

Authors: Xiangyu Zhang (National Renewable Energy Laboratory); Yue Chen (National Renewable Energy Laboratory); Andrey Bernstein (NREL)

Buildings Power & Energy Reinforcement Learning
ICLR 2023 Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models (Papers Track)
Abstract and authors: (click to expand)

Abstract: Modern machine learning models have started to consume incredible amounts of energy, thus incurring large carbon footprints (Strubell et al., 2019). To address this issue, we have created an energy estimation pipeline, which allows practitioners to estimate the energy needs of their models in advance, without actually running or training them. We accomplished this, by collecting high-quality energy data and building a first baseline model, capable of predicting the energy consumption of DL models by accumulating their estimated layer-wise energies.

Authors: Johannes Getzner (Technical University of Munich); Bertrand Charpentier (Technical University of Munich); Stephan Günnemann (Technical University of Munich)

Power & Energy Data Mining
ICLR 2023 Topology Estimation from Voltage Edge Sensing for Resource-Constrained Grids (Papers Track)
Abstract and authors: (click to expand)

Abstract: Electric grids are the conduit for renewable energy delivery and will play a crucial role in mitigating climate change. To do so successfully in resource-constrained low- and middle-income countries (LMICs), increasing operational efficiency is key. Such efficiency demands evolving knowledge of the grid’s state, of which topology---how points on the network are physically connected---is fundamental. In LMICs, knowledge of distribution topology is limited and established methods for topology estimation rely on expensive sensing infrastructure, such as smart meters or PMUs, that are inaccessible at scale. This paper lays the foundation for topology estimation from more accessible data: outlet-level voltage magnitude measurements. It presents a graph-based algorithm and explanatory visualization using the Fielder vector for estimating and communicating topological proximity from this data. We demonstrate the method on a real dataset collected in Accra, Ghana, thus opening the possibility of globally accessible, cutting-edge grid monitoring through non-traditional sensing strategies coupled with ML.

Authors: Mohini S Bariya (nLine); Genevieve Flaspohler (nLine)

Unsupervised & Semi-Supervised Learning Climate Justice Time-series Analysis
ICLR 2023 Chasing Low-Carbon Electricity for Practical and Sustainable DNN Training (Papers Track)
Abstract and authors: (click to expand)

Abstract: Deep learning has experienced significant growth in recent years, resulting in increased energy consumption and carbon emission from the use of GPUs for training deep neural networks (DNNs). Answering the call for sustainability, conventional solutions have attempted to move training jobs to locations or time frames with lower carbon intensity. However, moving jobs to other locations may not always be feasible due to large dataset sizes or data regulations. Moreover, postponing training can negatively impact application service quality because the DNNs backing the service are not updated in a timely fashion. In this work, we present a practical solution that reduces the carbon footprint of DNN training without migrating or postponing jobs. Specifically, our solution observes real-time carbon intensity shifts during training and controls the energy consumption of GPUs, thereby reducing carbon footprint while maintaining training performance. Furthermore, in order to proactively adapt to shifting carbon intensity, we propose a lightweight machine learning algorithm that predicts the carbon intensity of the upcoming time frame. Our solution, Chase, reduces the total carbon footprint of training ResNet-50 on ImageNet by 13.6% while only increasing training time by 2.5%.

Authors: Zhenning Yang (University of Michigan, Ann Arbor); Luoxi Meng (University of Michigan, Ann Arbor); Jae-Won Chung (University of Michigan, Ann Arbor); Mosharaf Chowdhury (University of Michigan, Ann Arbor)

Time-series Analysis
ICLR 2023 SOLAR PANEL MAPPING VIA ORIENTED OBJECT DETECTION (Papers Track)
Abstract and authors: (click to expand)

Abstract: Maintaining the integrity of solar power plants is a vital component in dealing with the current climate crisis. This process begins with analysts creating a de- tailed map of a plant with the coordinates of every solar panel, making it possible to quickly locate and mitigate potential faulty solar panels. However, this task is extremely tedious and is not scalable for the ever increasing capacity of so- lar power across the globe. Therefore, we propose an end-to-end deep learning framework for detecting individual solar panels using a rotated object detection architecture. We evaluate our approach on a diverse dataset of solar power plants collected from across the United States and report a mAP score of 83.3%.

Authors: Conor Wallace (DroneBase); Isaac Corley (University of Texas at San Antonio); Jonathan Lwowski (DroneBase)

Computer Vision & Remote Sensing
ICLR 2023 Disentangling observation biases to monitor spatio-temporal shifts in species distributions (Proposals Track)
Abstract and authors: (click to expand)

Abstract: The accelerated pace of environmental change due to anthropogenic activities makes it more important than ever to understand current and future ecosystem dynamics at a global scale. Species observations stemming from citizen science platforms are increasingly leveraged to gather information about the geographic distributions of many species. However, their usability is limited by the strong biases inherent to these community-driven efforts. These biases in the sampling effort are often treated as noise that has to be compensated for. In this project, we posit that better modelling the sampling effort (including the usage of the different platforms across countries, local accessibility, attractiveness of the location for platform users, affinity of different user groups for different species, etc.) is the key towards improving Species Distribution Models (SDM) using observations from citizen science platforms, thus opening up the possibility of leveraging them to monitor changes in species distributions and population densities.

Authors: Diego Marcos (Inria); Christophe Botella (); Ilan Havinga (Wageningen University); Dino Ienco (INRAE); Cassio F. Dantas (TETIS, INRAE, Univ Montpellier); Pierre Alliez (INRIA Sophie-Antipolis, France); Alexis Joly (INRIA, FR)

Causal & Bayesian Methods Earth Observation & Monitoring Computer Vision & Remote Sensing Hybrid Physical Models
ICLR 2023 Mapping global innovation networks around clean energy technologies (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Reaching net zero emissions requires rapid innovation and scale-up of clean tech. In this context, clean tech innovation networks (CTINs) can play a crucial role by pooling necessary resources and competences and enabling knowledge transfers between different actors. However, existing evidence on CTINs is limited due to a lack of comprehensive data. Here, we develop a machine learning framework to identify CTINs from announcements on social media to map the global CTIN landscape. Specifically, we classify the social media announcements regarding the type of technology (e.g., hydrogen, solar), interaction type (e.g., equity investment, R\&D collaboration), and status (e.g., commencement, update). We then extract referenced organizations via entity recognition. Thereby, we generate a large-scale dataset of CTINs across different technologies, countries, and over time. This allows us to compare characteristics of CTINs, such as the geographic proximity of actors, and to investigate the association between network evolution and technology innovation and diffusion. As a direct implication, our work helps policy makers to promote CTINs by identifying current barriers and needs.

Authors: Malte Toetzke (ETH Zurich); Francesco Re (ETH Zurich); Benedict Probst (ETH Zurich); Stefan Feuerriegel (LMU Munich); Laura Diaz Anadon (University of Cambridge); Volker Hoffmann (ETH Zurich)

Climate Finance & Economics Supply Chains Natural Language Processing
ICLR 2023 Widespread increases in future wildfire risk to global forest carbon offset projects revealed by explainable AI (Papers Track)
Abstract and authors: (click to expand)

Abstract: Carbon offset programs are critical in the fight against climate change. One emerging threat to the long-term stability and viability of forest carbon offset projects is wildfires, which can release large amounts of carbon and limit the efficacy of associated offsetting credits. However, analysis of wildfire risk to forest carbon projects is challenging because existing models for forecasting long-term fire risk are limited in predictive accuracy. Therefore, we propose an explainable artificial intelligence (XAI) model trained on 7 million global satellite wildfire observations. Validation results suggest substantial potential for high resolution, enhanced accuracy projections of global wildfire risk, and the model outperforms the U.S. National Center for Atmospheric Research's leading fire model. Applied to a collection of 190 global forest carbon projects, we find that fire exposure is projected to increase 55% [37-76%] by 2080 under a mid-range scenario (SSP2-4.5). Our results indicate the large wildfire carbon project damages seen in the past decade are likely to become more frequent as forests become hotter and drier. In response, we hope the model can support wildfire managers, policymakers, and carbon market analysts to preemptively quantify and mitigate long-term permanence risks to forest carbon projects.

Authors: Tristan Ballard (Sust Inc); Gopal Erinjippurath (Sust Global); Matthew W Cooper (Sust Global); Chris Lowrie (Sust Global)

Climate Finance & Economics Forests Interpretable ML Land Use
ICLR 2023 A High-Resolution, Data-Driven Model of Urban Carbon Emissions (Papers Track) Best Pathway to Impact
Abstract and authors: (click to expand)

Abstract: Cities represent both a fundamental contributor to greenhouse (GHG) emissions and a catalyst for climate action. Many global cities have outlined sustainability and climate change mitigation plans, focusing on energy efficiency, shifting away from fossil fuels, and prioritizing environmental and social justice. To achieve broad-based and equitable carbon emissions reductions and sustainability goals, new data-driven methodologies are needed to identify and target efficiency and carbon reduction opportunities in the built environment at the building, neighborhood, and city-scale. Our methodology integrates data from numerous data sources and develops data-driven and physical models of energy use and carbon emissions from buildings and transportation to generate a high spatiotemporal resolution model of urban greenhouse gas emissions. The method and data tool are designed to support city leaders and urban policymakers with an unprecedented view of localized carbon emissions to enable data-driven and evidenced-based climate action.

Authors: Bartosz Bonczak (New York University); Boyeong Hong (New York University); Constantine E. Kontokosta (New York University)

Hybrid Physical Models Climate Science & Modeling Data Mining
ICLR 2023 ClimaX: A foundation model for weather and climate (Papers Track)
Abstract and authors: (click to expand)

Abstract: Recent data-driven approaches based on machine learning aim to directly solve a downstream forecasting or projection task by learning a data-driven functional mapping using deep neural networks. However, these networks are trained using curated and homogeneous climate datasets for specific spatiotemporal tasks, and thus lack the generality of currently used physics-informed numerical models for weather and climate modeling. We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatiotemporal coverage, and physical groundings. ClimaX extends the Transformer architecture with novel encoding and aggregation blocks that allow effective use of available compute and data while maintaining general utility. ClimaX is pre-trained with a self-supervised learning objective on climate datasets derived from CMIP6. The pretrained ClimaX can then be fine-tuned to address a breadth of climate and weather tasks, including those that involve atmospheric variables and spatiotemporal scales unseen during pretraining. Compared to existing data-driven baselines, we show that this generality in ClimaX results in superior performance on benchmarks for weather forecasting and climate projections.

Authors: Tung Nguyen (University of California, Los Angeles); Johannes Brandstetter (Microsoft Research); Ashish Kapoor (Microsoft); Jayesh Gupta (Microsoft Research); Aditya Grover (UCLA)

Climate Science & Modeling Meta- and Transfer Learning
ICLR 2023 Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplastics (Papers Track)
Abstract and authors: (click to expand)

Abstract: Finding a balance between collaboration and competition is crucial for artificial agents in many real-world applications. We investigate this using a Multi-Agent Reinforcement Learning (MARL) setup on the back of a high-impact problem. The accumulation and yearly growth of plastic in the ocean cause irreparable damage to many aspects of oceanic health and the marina system. To prevent further damage, we need to find ways to reduce macroplastics from known plastic patches in the ocean. Here we propose a Graph Neural Network (GNN) based communication mechanism that increases the agents' observation space. In our custom environment, agents control a plastic collecting vessel. The communication mechanism enables agents to develop a communication protocol using a binary signal. While the goal of the agent collective is to clean up as much as possible, agents are rewarded for the individual amount of macroplastics collected. Hence agents have to learn to communicate effectively while maintaining high individual performance. We compare our proposed communication mechanism with a multi-agent baseline without the ability to communicate. Results show communication enables collaboration and increases collective performance significantly. This means agents have learned the importance of communication and found a balance between collaboration and competition.

Authors: Philipp D Siedler (Aleph Alpha)

Reinforcement Learning Oceans & Marine Systems
ICLR 2023 Sub-seasonal to seasonal forecasts through self-supervised learning (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Sub-seasonal to seasonal (S2S) weather forecasts are an important decision- making tool that informs economical and logistical planning in agriculture, energy management, and disaster mitigation. They are issued on time scales of weeks to months and differ from short-term weather forecasts in two important ways: (i) the dynamics of the atmosphere on these timescales can be described only statistically and (ii) these dynamics are characterized by large-scale phenomena in both space and time. While deep learning (DL) has shown promising results in short-term weather forecasting, DL-based S2S forecasts are challenged by comparatively small volumes of available training data and large fluctuations in predictability due to atmospheric conditions. In order to develop more reliable S2S predictions that leverage current advances in DL, we propose to utilize the masked auto-encoder (MAE) framework to learn generic representations of large-scale atmospheric phenomena from high resolution global data. Besides exploring the suitability of the learned representations for S2S forecasting, we will also examine whether they account for climatic phenomena (e.g., the Madden-Julian Oscillation) that are known to increase predictability on S2S timescales.

Authors: Jannik Thuemmel (University of Tuebingen); Felix Strnad (Potsdam Institute for Climate Impact Research); Jakob Schlör (Eberhard Karls Universität Tübingen); Martin V. Butz (University of Tübingen); Bedartha Goswami (University of Tübingen)

Unsupervised & Semi-Supervised Learning Climate Science & Modeling Extreme Weather Interpretable ML
ICLR 2023 Mining Effective Strategies for Climate Change Communication (Papers Track)
Abstract and authors: (click to expand)

Abstract: With the goal of understanding effective strategies to communicate about climate change, we build interpretable models to rank tweets related to climate change with respect to the engagement they generate. Our models are based on the Bradley-Terry model of pairwise comparison outcomes and use a combination of the tweets’ topic and metadata features to do the ranking. To remove confounding factors related to author popularity and minimise noise, they are trained on pairs of tweets that are from the same author and around the same time period and have a sufficiently large difference in engagement. The models achieve good accuracy on a held-out set of pairs. We show that we can interpret the parameters of the trained model to identify the topic and metadata features that contribute to high engagement. Among other observations, we see that topics related to climate projections, human cost and deaths tend to have low engagement while those related to mitigation and adaptation strategies have high engagement. We hope the insights gained from this study will help craft effective climate communication to promote engagement, thereby lending strength to efforts to tackle climate change.

Authors: Aswin Suresh (EPFL); Lazar Milikic (EPFL); Francis Murray (EPFL); Yurui Zhu (EPFL); Matthias Grossglauser (École Polytechnique Fédérale de Lausanne (EPFL))

Natural Language Processing Behavioral and Social Science Public Policy Societal Adaptation & Resilience Data Mining Interpretable ML Unsupervised & Semi-Supervised Learning
ICLR 2023 Graph-Based Deep Learning for Sea Surface Temperature Forecasts (Papers Track)
Abstract and authors: (click to expand)

Abstract: Sea surface temperature (SST) forecasts help with managing the marine ecosystem and the aquaculture impacted by anthropogenic climate change. Numerical dynamical models are resource intensive for SST forecasts; machine learning (ML) models could reduce high computational requirements and have been in the focus of the research community recently. ML models normally require a large amount of data for training. Environmental data are collected on regularly-spaced grids, so early work mainly used grid-based deep learning (DL) for prediction. However, both grid data and the corresponding DL approaches have inherent problems. As geometric DL has emerged, graphs as a more generalized data structure and graph neural networks (GNNs) have been introduced to the spatiotemporal domains. In this work, we preliminarily explored graph re-sampling and GNNs for global SST forecasts, and GNNs show better one month ahead SST prediction than the persistence model in most oceans in terms of root mean square errors.

Authors: Ding Ning (University of Canterbury); Varvara Vetrova (University of Canterbury); Karin Bryan (University of Waikato)

Data Mining Climate Science & Modeling Oceans & Marine Systems Time-series Analysis
ICLR 2023 Activity-Based Recommendations for the Reduction of CO2 Emissions in Private Households (Papers Track)
Abstract and authors: (click to expand)

Abstract: This paper proposes an activity prediction framework for a multi-agent recommendation system to tackle the energy-efficiency problem in residential buildings. Our system generates an activity-shifting schedule based on the social practices from the users’ domestic life. We further provide a utility option for the recommender system to focus on saving CO2 emissions or energy costs, or both. The empirical results show that while focusing on the reduction of CO2 emissions, the system provides an average of 12% of emission savings and 7% of electricity cost savings. When concentrating on energy costs, 6% of emission savings and 20% of electricity cost savings are possible for the studied households.

Authors: Alona Zharova (Humboldt University of Berlin); Laura Löschmann (Humboldt University of Berlin)

Recommender Systems Buildings Power & Energy
ICLR 2023 Data-driven mean-variability optimization of PV portfolios with automatic differentiation (Papers Track)
Abstract and authors: (click to expand)

Abstract: Increasing PV capacities has a crucial role to reach carbon-neutral energy systems. To promote PV expansion, policy designs have been developed which rely on energy yield maximization to increase the total PV energy supply in energy systems. Focusing on yield maximization, however, ignores negative side-effects such as an increased variability due to similar-orientated PV systems at clustered regions. This can lead to costly ancillary services and thereby reduces the acceptance of renewable energy. This paper suggests to rethink PV portfolio designs by deriving mean-variability hedged PV portfolios with smartly orientated tilt and azimuth angles. Based on a data-driven method inspired from modern portfolio theory, we formulate the problem as a biobjective, non-convex optimization problem which is solved based on automatically differentiating the physical PV conversion model subject to individual tilt and azimuth angles. To illustrate the performance of the proposed method, a case study is designed to derive efficient frontiers in the mean-variability spectrum of Germany's PV portfolio based on representative grid points. The proposed method allows decision-makers to hedge between variability and yield in PV portfolio design decisions. This is the first study highlighting the problem of ignoring variability in PV portfolio expansion schemes and introduces a way to tackle this issue using modern methods inspired by Machine Learning.

Authors: Matthias Zech (German Aerospace Center (DLR), Institute of Networked Energy Systems); Lueder von Bremen (German Aerospace Center (DLR), Institute of Networked Energy Systems)

Power & Energy Climate Finance & Economics Interpretable ML
ICLR 2023 DiffESM: Conditional Emulation of Earth System Models with Diffusion Models (Papers Track)
Abstract and authors: (click to expand)

Abstract: Earth System Models (ESMs) are essential tools for understanding the impact of human actions on Earth's climate. One key application of these models is studying extreme weather events, such as heat waves or dry spells, which have significant socioeconomic and environmental consequences. However, the computational demands of running a sufficient number of simulations to analyze the risks are often prohibitive. In this paper we demonstrate that diffusion models -- a class of generative deep learning models -- can effectively emulate the spatio-temporal trends of ESMs under previously unseen climate scenarios, while only requiring a small fraction of the computational resources. We present a diffusion model that is conditioned on monthly averages of temperature or precipitation on a 96x96 global grid, and produces daily values that are both realistic and consistent with those averages. Our results show that the output from our diffusion model closely matches the spatio-temporal behavior of the ESM it emulates in terms of the frequency of phenomena such as heat waves, dry spells, or rainfall intensity.

Authors: Seth Bassetti (Western Washington University); Brian Hutchinson (Western Washington University); Claudia Tebaldi (Joint Global Change Research Institute); Ben Kravitz (Indiana University)

Climate Science & Modeling Generative Modeling
ICLR 2023 Emission-Constrained Optimization of Gas Systems with Input-Convex Neural Networks (Papers Track)
Abstract and authors: (click to expand)

Abstract: Planning optimization of gas networks under emission constraints prioritizes gas supply with the smallest emission footprint. As this problem includes complex gas flow physical laws, standard optimization solvers cannot guarantee convergence to a feasible solution, especially under strict emission constraints. To address this issue, we develop an input-convex neural network (ICNN) aided optimization routine which incorporates a set of trained ICNNs approximating the gas flow equations with high precision. Numerical tests on the Belgium gas network demonstrate that the ICNN-aided optimization dominates non-convex and relaxation-based solvers, with larger optimality gains pertaining to stricter emission targets.

Authors: Vladimir Dvorkin (Massachusetts Institute of Technology); Samuel C Chevalier (Technical University of Denmark); Spyros Chatzivasileiadis (Technical University of Denmark)

Power & Energy Supply Chains
ICLR 2023 Deep ensembles to improve uncertainty quantification of statistical downscaling models under climate change conditions (Papers Track)
Abstract and authors: (click to expand)

Abstract: Recently, deep learning has emerged as a promising tool for statistical downscaling, the set of methods for generating high-resolution climate fields from coarse low-resolution variables. Nevertheless, their ability to generalize to climate change conditions remains questionable, mainly due to the stationarity assumption. We propose deep ensembles as a simple method to improve the uncertainty quantification of statistical downscaling models. By better capturing uncertainty, statistical downscaling models allow for superior planning against extreme weather events, a source of various negative social and economic impacts. Since no observational future data exists, we rely on a pseudo reality experiment to assess the suitability of deep ensembles for quantifying the uncertainty of climate change projections. Deep ensembles allow for a better risk assessment, highly demanded by sectoral applications to tackle climate change.

Authors: Jose González-Abad (Instituto de Fı́sica de Cantabria (IFCA), CSIC-Universidad de Cantabria); Jorge Baño-Medina (Institute of Physics of Cantabria)

Uncertainty Quantification & Robustness Climate Science & Modeling
ICLR 2023 Bayesian Inference of Severe Hail in Australia (Papers Track)
Abstract and authors: (click to expand)

Abstract: Severe hailstorms are responsible for some of the most costly insured weather events in Australia and can cause significant damage to homes, businesses, and agriculture. However their response to climate change remains uncertain, in large part due to the challenges of observing severe hailstorms. We propose a novel Bayesian approach which explicitly models known biases and uncertainties of current hail observations to produce more realistic estimates of severe hail risk from existing observations. Training this model on data from south-east Queensland, Australia, suggests that previous analyses of severe hail that did not account for this uncertainty may produce poorly calibrated risk estimates. Preliminary evaluation on withheld data confirms that our model produces well-calibrated probabilities and is applicable out of sample. Whilst developed for hail, we highlight also the generality of our model and its potential applications to other severe weather phenomena and areas of climate change adaptation and mitigation.

Authors: Isabelle C Greco (University of New South Wales); Steven Sherwood (University of New South Wales); Timothy Raupach (University of New South Wales); Gab Abramowitz (University of New South Wales)

Causal & Bayesian Methods Climate Science & Modeling Disaster Management and Relief Earth Observation & Monitoring Extreme Weather Uncertainty Quantification & Robustness
ICLR 2023 Exploring the potential of neural networks for Species Distribution Modeling (Papers Track)
Abstract and authors: (click to expand)

Abstract: Species distribution models (SDMs) relate species occurrence data with environmental variables and are used to understand and predict species distributions across landscapes. While some machine learning models have been adopted by the SDM community, recent advances in neural networks may have untapped potential in this field. In this work, we compare the performance of multi-layer perceptron (MLP) neural networks to well-established SDM methods on a benchmark dataset spanning 225 species in six geographical regions. We also compare the performance of MLPs trained separately for each species to an equivalent model trained on a set of species and performing multi-label classification. Our results show that MLP models achieve comparable results to state-of-the-art SDM methods, such as MaxEnt. We also find that multi-species MLPs perform slightly better than single-species MLPs. This study indicates that neural networks, along with all their convenient and valuable characteristics, are worth considering for SDMs.

Authors: Robin Zbinden (EPFL); Nina van Tiel (EPFL); Benjamin Kellenberger (Yale University); Lloyd H Hughes (EPFL); Devis Tuia (EPFL)

Ecosystems & Biodiversity
ICLR 2023 Modelling Atmospheric Dynamics with Spherical Fourier Neural Operators (Papers Track)
Abstract and authors: (click to expand)

Abstract: Fourier Neural Operators (FNOs) have established themselves as an efficient method for learning resolution-independent operators in a wide range of scientific machine learning applications. This can be attributed to their ability to effectively model long-range dependencies in spatio-temporal data through computationally ef- ficient global convolutions. However, the use of discrete Fourier transforms (DFTs) in FNOs leads to spurious artifacts and pronounced dissipation when applied to spherical coordinates, due to the incorrect assumption of flat geometry. To ad- dress the issue, we introduce Spherical FNOs (SFNOs), which use the generalized Fourier transform for learning operators on spherical geometries. We demonstrate the effectiveness of the method for forecasting atmospheric dynamics, producing stable auto-regressive results for a simulated time of one year (1,460 steps) while retaining physically plausible dynamics. This development has significant implica- tions for machine learning-based climate dynamics emulation, which could play a crucial role in accelerating our response to climate change.

Authors: Boris Bonev (NVIDIA); Thorsten Kurth (Nvidia); Christian Hundt (NVIDIA AI Technology Center); Jaideep Pathak (NVIDIA Corporation); Maximilian Baust (NVIDIA); Karthik Kashinath (NVIDIA); Anima Anandkumar (NVIDIA/Caltech)

Hybrid Physical Models Climate Science & Modeling Extreme Weather
ICLR 2023 Distributed Reinforcement Learning for DC Open Energy Systems (Papers Track)
Abstract and authors: (click to expand)

Abstract: The direct current open energy system (DCOES) enables the production, storage, and exchange of renewable energy within local communities, which is helpful, especially in isolated villages and islands where centralized power supply is unavailable or unstable. As solar and wind energy production varies in time and space depending on the weather and the energy usage patterns differ for different households, how to store and exchange energy is an important research issue. In this work, we explore the use of deep reinforcement learning (DRL) for adaptive control of energy storage in local batteries and energy sharing through DC grids. We extend the Autonomous Power Interchange System (APIS) emulator from SonyCSL to combine it with reinforcement learning algorithms in each house. We implemented deep Q-network (DQN) and prioritized DQN to dynamically set the parameters of the real-time energy exchange protocol of APIS and tested it using the actual data collected from the DCOES in the faculty houses of Okinawa Institute of Science and Technology (OIST). The simulation results showed that RL agents outperformed the hand-tuned control strategy. Sharing average energy production, storage, and usage within the local community further improved efficiency. The implementation of DRL methods for adaptive energy storage and exchange can help reducing carbon emission and positively impact the climate.

Authors: Qiong Huang (Okinawa Institute of Science and Technology Graduate University); Kenji Doya (Okinawa Institute of Science and Technology)

Reinforcement Learning Power & Energy
ICLR 2023 Uncovering the Spatial and Temporal Variability of Wind Resources in Europe: A Web-Based Data-Mining Tool (Papers Track)
Abstract and authors: (click to expand)

Abstract: We introduce REmap-eu.app, a web-based data-mining visualization tool of the spatial and temporal variability of wind resources. It uses the latest open-access dataset of the daily wind capacity factor in 28 European countries between 1979 and 2019 and proposes several user-configurable visualizations of the temporal and spatial variations of the wind power capacity factor. The platform allows for a deep analysis of the distribution, the cross-country correlation, and the drivers of low wind power events. It offers an easy-to-use interface that makes it suitable for the needs of researchers and stakeholders. The tool is expected to be useful in identifying areas of high wind potential and possible challenges that may impact the large-scale deployment of wind turbines in Europe. Particular importance is given to the visualization of low wind power events and to the potential of cross-border cooperations in mitigating the variability of wind in the context of increasing reliance on weather-sensitive renewable energy sources.

Authors: Alban Puech (École Polytechnique); Jesse Read (Ecole Polytechnique)

Data Mining Climate Science & Modeling Power & Energy Time-series Analysis
ICLR 2023 Understanding forest resilience to drought with Shapley values (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Increases in drought frequency, intensity, and duration due to climate change are threatening forests around the world. Climate-driven tree mortality is associated with devastating ecological and societal consequences, including the loss of carbon sequestration, habitat provisioning, and water filtration services. A spatially fine-grained understanding of the site characteristics making forests more resilient to drought is still lacking. Furthermore, the complexity of drought effects on forests, which can be cumulative and delayed, demands investigation of the most appropriate drought indices. In this study, we aim to gain a better understanding of the temporal and spatial drivers of drought-induced changes in forest vitality using Shapley values, which allow for the relevance of predictors to be quantified locally. A better understanding of the contribution of meteorological and environmental factors to trees’ response to drought can support forest managers aiming to make forests more climate-resilient.

Authors: Stenka Vulova (Technische Universität Berlin); Alby Duarte Rocha (Technische Universität Berlin); Akpona Okujeni (Humboldt-Universität zu Berlin); Johannes Vogel (Freie Universität Berlin); Michael Förster (Technische Universität Berlin); Patrick Hostert (Humboldt-Universität zu Berlin); Birgit Kleinschmit (Technische Universität Berlin)

Forests Earth Observation & Monitoring Climate Science & Modeling Ecosystems & Biodiversity Extreme Weather Interpretable ML
ICLR 2023 Improving the spatial accuracy of extreme tropical cyclone rainfall in ERA5 using deep learning (Papers Track)
Abstract and authors: (click to expand)

Abstract: We propose a novel method for the bias adjustment and post-processing of gridded rainfall data products. Our method uses U-Net (a deep convolutional neural network) as a backbone, and a novel loss function given by the combination of a pixelwise bias component (Mean Absolute Error) and a spatial accuracy component (Fractions Skill Score). We evaluate the proposed approach by adjusting extreme rainfall from the popular ERA5 reanalysis dataset, using the multi-source observational dataset MSWEP as a target. We focus on a sample of extreme rainfall events induced by tropical cyclones and show that the proposed method significantly reduces both the MAE (by 16\%) and FSS (by 53\%) of ERA5.

Authors: Guido Ascenso (Politecnico di Milano); Andrea Ficchì (Politecnico di Milano); Matteo Giuliani (Politecnico di Milano); Leone Cavicchia (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)); Enrico Scoccimarro (Fondazione Centro Euro-Mediterraneo sui Cambiamenti Climatici (CMCC)); Andrea Castelletti (Politecnico di Milano)

Extreme Weather Climate Science & Modeling Computer Vision & Remote Sensing
ICLR 2023 XAI for transparent wind turbine power curve models (Papers Track)
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Abstract: Accurate wind turbine power curve models, which translate ambient conditions into turbine power output, are crucial for wind energy to scale and fulfill its proposed role in the global energy transition. While machine learning (ML) methods have shown significant advantages over parametric, physics-informed approaches, they are often criticized for being opaque "black boxes", which hinders their application in practice. We apply Shapley values, a popular explainable artificial intelligence (XAI) method, and the latest findings from XAI for regression models, to uncover the strategies ML models have learned from operational wind turbine data. Our findings reveal that the trend towards ever larger model architectures, driven by a focus on test set performance, can result in physically implausible model strategies. Therefore, we call for a more prominent role of XAI methods in model selection. Moreover, we propose a practical approach to utilize explanations for root cause analysis in the context of wind turbine performance monitoring. This can help to reduce downtime and increase the utilization of turbines in the field.

Authors: Simon Letzgus (Technische Universität Berlin)

Power & Energy Interpretable ML
ICLR 2023 Green AutoML for Plastic Litter Detection (Papers Track)
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Abstract: The world’s oceans are polluted with plastic waste and the detection of it is an important step toward removing it. Wolf et al. (2020) created a plastic waste dataset to develop a plastic detection system. Our work aims to improve the machine learning model by using Green Automated Machine Learning (AutoML). One aspect of Green-AutoML is to search for a machine learning pipeline, while also minimizing the carbon footprint. In this work, we train five standard neural architectures for image classification on the aforementioned plastic waste dataset. Subsequently, their performance and carbon footprints are compared to an Efficient Neural Architecture Search as a well-known AutoML approach. We show the potential of Green-AutoML by outperforming the original plastic detection system by 1.1% in accuracy and using 33 times fewer floating point operations at inference, and only 29% of the carbon emissions of the best-known baseline. This shows the large potential of AutoML on climate-change relevant applications and at the same time contributes to more efficient modern Deep Learning systems, saving substantial resources and reducing the carbon footprint.

Authors: Daphne Theodorakopoulos (German Research Centre for Artificial Intelligence (DFKI), Marine Perception Department and Leibniz University Hannover, Institute of Artificial Intelligence); Christoph Manß (German Research Centre for Artificial Intelligence (DFKI), Marine Perception Department); Frederic Stahl (German Research Centre for Artificial Intelligence (DFKI), Marine Perception Department); Marius Lindauer (Leibniz University Hannover)

Oceans & Marine Systems Computer Vision & Remote Sensing
ICLR 2023 Robustly modeling the nonlinear impact of climate change on agriculture by combining econometrics and machine learning (Proposals Track)
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Abstract: Climate change is expected to have a dramatic impact on agricultural production; however, due to natural complexity, the exact avenues and relative strengths by which this will happen are still unknown. The development of accurate forecasting models is thus of great importance to enable policy makers to design effective interventions. To date, most machine learning methods aimed at tackling this problem lack a consideration of causal structure, thereby making them unreliable for the types of counterfactual analysis necessary when making policy decisions. Econometrics has developed robust techniques for estimating cause-effect relations in time-series, specifically through the use of cointegration analysis and Granger causality. However, these methods are frequently limited in flexibility, especially in the estimation of nonlinear relationships. In this work, we propose to integrate the non-linear function approximators with the robust causal estimation methods to ultimately develop an accurate agricultural forecasting model capable of robust counterfactual analysis. This method would be a valuable new asset for government and industrial stakeholders to understand how climate change impacts agricultural production.

Authors: Benedetta Francesconi (Independent Researcher); Ying-Jung C Deweese (Descartes Labs / Georgia Insititute of Technology)

Agriculture & Food Climate Science & Modeling Public Policy Societal Adaptation & Resilience Time-series Analysis
ICLR 2023 Towards Green, Accurate, and Efficient AI Models Through Multi-Objective Optimization (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Machine learning is one of the fastest growing services in modern hyperscale data centers. While AI’s exponential scaling has enabled unprecedented modeling capabilities across computer vision, natural language processing, protein modeling, personalized recommendation, it comes at the expense of significant energy and environmental footprints. This work aims to co-optimize machine learning models in terms of their accuracy, compute efficiency, and environmental sustainability by using multi-objective bayesian optimization. We aim to extend current multi-objective optimization frameworks, such as the openly available Ax (adaptive experimentation) platform to balance accuracy, efficiency, and environmental sustainability of deep neural networks. In order to optimize for environmental sustainability we will consider the impact across AI model life cycles (e.g., training, inference) and hardware life cycles (e.g., manufacturing, operational use). Given this is a research proposal, we expect to demonstrate that designing for sustainable AI models yields distinct optimal neural network architectures than ones designed for accuracy and efficiency given the external impacts of varying renewable energy and tradeoffs between compute and storage for embodied carbon overheads.

Authors: Udit Gupta (Harvard University); Daniel R Jiang (Meta); Maximilian Balandat (Facebook); Carole-Jean Wu (Meta AI)

ICLR 2023 EfficientTempNet: Temporal Super-Resolution of Radar Rainfall (Papers Track)
Abstract and authors: (click to expand)

Abstract: Rainfall data collected by various remote sensing instruments such as radars or satellites has different space-time resolutions. This study aims to improve the temporal resolution of radar rainfall products to help with more accurate climate change modeling and studies. In this direction, we introduce a solution based on EfficientNetV2, namely EfficientTempNet, to increase the temporal resolution of radar-based rainfall products from 10 minutes to 5 minutes. We tested EfficientRainNet over a dataset for the state of Iowa, US, and compared its performance to three different baselines to show that EfficientTempNet presents a viable option for better climate change monitoring.

Authors: Bekir Z Demiray (University of Iowa); Muhammed A Sit (The University of Iowa); Ibrahim Demir (University of Iowa)

Earth Observation & Monitoring Climate Science & Modeling Extreme Weather Computer Vision & Remote Sensing Time-series Analysis
ICLR 2023 Bird Distribution Modelling using Remote Sensing and Citizen Science data (Papers Track) Overall Best Paper
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Abstract: Climate change is a major driver of biodiversity loss, changing the geographic range and abundance of many species. However, there remain significant knowl- edge gaps about the distribution of species, due principally to the amount of effort and expertise required for traditional field monitoring. We propose an approach leveraging computer vision to improve species distribution modelling, combining the wide availability of remote sensing data with sparse on-ground citizen science data from .We introduce a novel task and dataset for mapping US bird species to their habitats by predicting species encounter rates from satellite images, along with baseline models which demonstrate the power of our approach. Our methods open up possibilities for scalably modelling ecosystems properties worldwide.

Authors: Mélisande Teng (Mila, Université de Montréal); Amna Elmustafa (African Institute for Mathematical Science); Benjamin Akera (McGill University); Hugo Larochelle (UdeS); David Rolnick (McGill University, Mila)

Ecosystems & Biodiversity Computer Vision & Remote Sensing
ICLR 2023 Efficient HVAC Control with Deep Reinforcement Learning and EnergyPlus (Papers Track)
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Abstract: Heating and cooling comprise a significant fraction of the energy consumed by buildings, which in turn account for a significant fraction of society’s energy use. Most building heating, ventilation, and air conditioning (HVAC) systems use standard control schemes that meet basic operating constraints and comfort requirements but with suboptimal efficiency. Deep reinforcement learning (DRL) has shown immense potential for high-performing control in a variety of simulated settings, but has not been widely deployed for real-world control. Here we provide two contributions toward increasing the viability of real-world, DRL-based HVAC control, leveraging the EnergyPlus building simulator. First, we use the new EnergyPlus Python API to implement a first-of-its-kind, purely Python-based EnergyPlus DRL learning framework capable of generalizing to a wide variety of building configurations and weather scenarios. Second, we demonstrate an approach to constrained learning for this setting, removing the requirement to tune reward functions in order to maximize energy efficiency given temperature constraints. We tested our framework on realistic building models of a data center, an office building, and a secondary school. In each case, trained agents maintained temperature control while achieving energy savings relative to standard approaches.

Authors: Jared Markowitz (Johns Hopkins University Applied Physics Laboratory); Nathan Drenkow (Johns Hopkins University Applied Physics Laboratory)

Buildings Reinforcement Learning
ICLR 2023 Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling (Papers Track)
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Abstract: Running climate simulations informs us of future climate change. However, it is computationally expensive to resolve complex climate processes numerically. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations. So far, all neural network downscaling models can only downscale input samples with a pre-defined upsampling factor. In this work, we propose a Fourier neural operator downscaling model. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high-resolutions. Evaluated on Navier-Stokes equation solution data and ERA5 water content data, our downscaling model demonstrates better performance than widely used convolutional and adversarial generative super-resolution models in both learned and zero-shot downscaling. Our model's performance is further boosted when a constraint layer is applied. In the end, we show that by combining our downscaling model with a low-resolution numerical PDE solver, the downscaled solution outperforms the solution of the state-of-the-art high-resolution data-driven solver. Our model can be used to cheaply and accurately generate arbitrarily high-resolution climate simulation data with fast-running low-resolution simulation as input.

Authors: Qidong Yang (New York University); Paula Harder (Fraunhofer ITWM); Venkatesh Ramesh (University of Montreal, Mila); Alex Hernandez-Garcia (Mila - Quebec AI Institute); Daniela Szwarcman (IBM Research); Prasanna Sattigeri (IBM Research); Campbell D Watson (IBM Reserch); David Rolnick (McGill University, Mila)

Climate Science & Modeling Earth Observation & Monitoring Computer Vision & Remote Sensing Generative Modeling
ICLR 2023 Data-driven multiscale modeling of subgrid parameterizations in climate models (Papers Track) Best ML Innovation
Abstract and authors: (click to expand)

Abstract: Subgrid parameterizations that represent physical processes occurring below the resolution of current climate models are an important component in producing accurate, long-term predictions for the climate. A variety of approaches have been tested to design these components, including deep learning methods. In this work, we evaluate a proof of concept illustrating a multiscale approach to this prediction problem. We train neural networks to predict subgrid forcing values on a testbed model and examine improvements in prediction accuracy which can be obtained by using additional information in both fine-to-coarse and coarse-to-fine directions.

Authors: Karl Otness (New York University); Laure Zanna (NYU); Joan Bruna (NYU)

Climate Science & Modeling Hybrid Physical Models
ICLR 2023 Decision-aware uncertainty-calibrated deep learning for robust energy system operation (Proposals Track)
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Abstract: Decision-making under uncertainty is an important problem that arises in many domains. Achieving robustness guarantees requires well-calibrated uncertainties, which can be difficult to achieve in high-capacity prediction models such as deep neural networks. This paper proposes an end-to-end approach for learning uncertainty-calibrated deep learning models that directly optimizes a downstream decision-making objective with provable robustness. We also propose two concrete applications in energy system operations, including a grid scheduling task as well as an energy storage arbitrage task. As renewable wind and solar generation increasingly proliferate and their variability penetrates the energy grid, learning uncertainty-aware predictive models becomes increasingly crucial for maintaining efficient and reliable grid operation.

Authors: Christopher Yeh (California Institute of Technology); Nicolas Christianson (California Institute of Technology); Steven Low (California Institute of Technology); Adam Wierman (California Institute of Technology); Yisong Yue (Caltech)

Power & Energy Reinforcement Learning
ICLR 2023 Multi-Agent Deep Reinforcement Learning for Solar-Battery System to Mitigate Solar Curtailment in Real-Time Electricity Market (Papers Track)
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Abstract: The increased uptake of solar energy in the energy transition towards decarbonization has caused the issue of solar photovoltaic (PV) curtailments, resulting in significant economic losses and hindering the energy transition. To overcome this issue, battery energy storage systems (BESS) can serve as onsite backup sources for solar farms. However, the backup role of the BESS significantly limits its economic value, disincentivizing the BESS deployment due to high investment costs. Hence, it is essential to effectively reduce solar curtailment while ensuring viable operations of the BESS.

Authors: Jinhao Li (Monash University); Changlong Wang (Monash University); Hao Wang (Monash University)

Power & Energy Reinforcement Learning
ICLR 2023 Projecting the climate penalty on pm2.5 pollution with spatial deep learning (Proposals Track)
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Abstract: The climate penalty measures the effects of a changing climate on air quality due to the interaction of pollution with climate factors, independently of future changes in emissions. This work introduces a statistical framework for estimating the climate penalty on soot pollution (PM 2.5), which has been linked to respiratory and cardiovascular diseases and premature mortality. The framework evaluates the disparities in future PM 2.5 exposure across racial/ethnic and income groups---an important step towards informing mitigation public health policy and promoting environmental equity in addressing the effects of climate change. The proposed methodology aims to improve existing statistical-based methods for estimating the climate penalty using an expressive and scalable predictive model based on spatial deep learning with spatiotemporal trend estimation. The proposed approach will (1) use higher-resolution climate inputs, which current statistical methods to estimate the climate penalty approaches cannot accommodate; (2) integrate additional predictive data sources such as demographics, geology, and land use; (3) consider regional dependencies and synoptic weather patterns influencing PM 2.5, deconvolving the effects of climate change from increasing air quality regulations and other sources of unmeasured spatial heterogeneity.

Authors: Mauricio Tec (Harvard University); Riccardo Cadei (Harvard University); Francesca Dominici (Harvard University); Corwin Zigler (University of Texas at Austin)

Climate Justice Health Computer Vision & Remote Sensing Time-series Analysis
ICLR 2023 On the impact of small-data diversity on forecasts: evidence from meteorologically-driven electricity demand in Mediterranean zones. (Papers Track)
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Abstract: In this paper, we compare the improvement of probabilistic electricity demand forecasts for three specific coastal and island regions using raw and pre-computed meteorological features based on empirically-tested formulations drawn from climate science literature. Typically for the general task of time-series forecasting with strong weather/climate drivers, go-to models like the Autoregressive Integrated Moving Average (ARIMA) model are built with assumptions of how independent variables will affect a dependent one and are at best encoded with a handful of exogenous features with known impact. Depending on the geographical region and/or cultural practices of a population, such a selection process may yield a non-optimal feature set which would ultimately drive a weak impact on underline demand forecasts. The aim of this work is to assess the impact of a documented set of meteorological features on electricity demand using deep learning models in comparative studies. Leveraging the defining computational architecture of the Temporal Fusion Transformer (TFT), we discover the unimportance of weather features for improving probabilistic forecasts for the targeted regions. However, through experimentation, we discover that the more stable electricity demand of the coastal Mediterranean regions, the Ceuta and Melilla autonomous cities in Morocco, improved the forecast accuracy of the strongly tourist-driven electricity demand for the Balearic islands located in Spain during the time of travel restrictions (i.e., during COVID19 (2020))--a root mean squared error (RMSE) from ~0.090 to ~0.012 with a substantially improved 10th/90th quantile bounding.

Authors: Reginald Bryant (IBM Research - Africa); Julian Kuehnert (IBM Research)

Time-series Analysis Cities & Urban Planning Climate Science & Modeling Extreme Weather Societal Adaptation & Resilience Uncertainty Quantification & Robustness
ICLR 2023 Artificial Intelligence in Tropical Cyclone Forecasting (Proposals Track)
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Abstract: Tropical cyclones (TC) in Bangladesh and other developing nations harm property and human lives. Theoretically, artificial intelligence (AI) can anticipate TC using tracking, intensity, and cyclone aftereffect phenomena. Although AI has a significant impact on predicting, poorer nations have struggled to adjust to its real-world applications. The interpretability of such a solution from an AI-based solution is the main factor in that situation, together with the infrastructure. Explainable AI has been extensively employed in the medical field because the outcome is so important. We believe that using explainable AI in TC forecasting is equally important as one large forecast can cause the thought of life loss. Additionally, it will improve the long-term viability of the AI-based weather forecasting system. To be more specific, we want to employ explainable AI in every way feasible in this study to address the problems of TC forecasting, intensity estimate, and tracking. We'll do this by using the graph neural network. The adoption of AI-based solutions in underdeveloped nations will be aided by this solution, which will boost their acceptance. With this effort, we also hope to tackle the challenge of sustainable AI in order to tackle the issue of climate change on a larger scale. However, Cyclone forecasting might be transformed by sustainable AI, guaranteeing precise and early predictions to lessen the effects of these deadly storms. The examination of vast volumes of meteorological data to increase forecasting accuracy is made possible by the combination of AI algorithms and cutting-edge technologies like machine learning and big data analytics. Improved accuracy is one of the main advantages of sustainable AI for cyclone prediction. To provide more precise forecasts, AI systems can evaluate a lot of meteorological data, including satellite imagery and ocean temperature readings.

Authors: Dr. Nusrat Sharmin (Military Institute of Science and Technology); Professor Dr. Md. Mahbubur Rahman Rahman (Military Institute of Science and Technology (MIST)); Sabbir Rahman (Military Institute of Science and Technology); Mokhlesur Rahman (Military Institute of Science and Technology)

Climate Science & Modeling Disaster Management and Relief Interpretable ML
NeurIPS 2022 Function Approximations for Reinforcement Learning Controller for Wave Energy Converters (Papers Track)
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Abstract: Waves are a more consistent form of clean energy than wind and solar and the latest Wave Energy Converters (WEC) platforms like CETO 6 have evolved into complex multi-generator designs with a high energy capture potential for financial viability. Multi-Agent Reinforcement Learning (MARL) controller can handle these complexities and control the WEC optimally unlike the default engineering controllers like Spring Damper which suffer from lower energy capture and mechanical stress from the spinning yaw motion. In this paper, we look beyond the normal hyper-parameter and MARL agent tuning and explored the most suitable architecture for the neural network function approximators for the policy and critic networks of MARL which act as its brain. We found that unlike the commonly used fully connected network (FCN) for MARL, the sequential models like transformers and LSTMs can model the WEC system dynamics better. Our novel transformer architecture, Skip Transformer-XL (STrXL), with several gated residual connections in and around the transformer block performed better than the state-of-the-art with faster training convergence. STrXL boosts energy efficiency by an average of 25% to 28% over the existing spring damper (SD) controller for waves at different angles and almost eliminated the mechanical stress from the rotational yaw motion, saving costly maintenance on open seas, and thus reducing the Levelized Cost of wave energy (LCOE). Demo: https://tinyurl.com/4s4mmb9v

Authors: Soumyendu Sarkar (Hewlett Packard Enterprise); Vineet Gundecha (Hewlett Packard Enterpise); Alexander Shmakov (UC Irvine); Sahand Ghorbanpour (Hewlett Packard Enterprise); Ashwin Ramesh Babu (Hewlett Packard Enterprise Labs); Alexandre Pichard (Carnegie Clean Energy); Mathieu Cocho (Carnegie Clean Energy)

Power & Energy Reinforcement Learning
NeurIPS 2022 Image-Based Soil Organic Carbon Estimation from Multispectral Satellite Images with Fourier Neural Operator and Structural Similarity (Papers Track)
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Abstract: Soil organic carbon (SOC) sequestration is the transfer and storage of atmospheric carbon dioxide in soils, which plays an important role in climate change mitigation. SOC concentration can be improved by proper land use, thus it is beneficial if SOC can be estimated at a regional or global scale. As multispectral satellite data can provide SOC-related information such as vegetation and soil properties at a global scale, estimation of SOC through satellite data has been explored as an alternative to manual soil sampling. Although existing works show promising results, most studies are based on pixel-based approaches with traditional machine learning methods, and convolutional neural networks (CNNs) are seldom used. To study the advantages of using CNNs on SOC remote sensing, in this paper, we propose the FNO-DenseNet based on the state-of-the-art Fourier neural operator (FNO). By combining the advantages of the FNO and DenseNet, the FNO-DenseNet outperformed the FNO in our experiments with hundreds of times fewer parameters. The FNO-DenseNet also outperformed a pixel-based random forest by 18% in the mean absolute percentage error. To the best of our knowledge, this is the first work of applying the FNO on SOC remote sensing.

Authors: Ken C. L. Wong (IBM Research – Almaden Research Center); Levente Klein (IBM Research); Ademir Ferreira da Silva (IBM Research); Hongzhi Wang (IBM Almaden Research Center); Jitendra Singh (IBM Research - India); Tanveer Syeda-Mahmood (IBM Research)

Carbon Capture & Sequestration Computer Vision & Remote Sensing
NeurIPS 2022 SolarDK: A high-resolution urban solar panel image classification and localization dataset (Papers Track)
Abstract and authors: (click to expand)

Abstract: The body of research on classification of solar panel arrays from aerial imagery is increasing, yet there are still not many public benchmark datasets. This paper introduces two novel benchmark datasets for classifying and localizing solar panel arrays in Denmark: A human annotated dataset for classification and segmentation, as well as a classification dataset acquired using self-reported data from the Danish national building registry. We explore the performance of prior works on the new benchmark dataset, and present results after fine-tuning models using a similar approach as recent works. Furthermore, we train models of newer architectures and provide benchmark baselines to our datasets in several scenarios. We believe the release of these datasets may improve future research in both local and global geospatial domains for identifying and mapping of solar panel arrays from aerial imagery. The data is accessible at https://osf.io/aj539/.

Authors: Maxim MK Khomiakov (DTU); Julius Radzikowski (DTU); Carl Schmidt (DTU); Mathias Sørensen (DTU); Mads Andersen (DTU); Michael Andersen (Technical University of Denmark); Jes Frellsen (Technical University of Denmark)

Buildings Power & Energy Computer Vision & Remote Sensing
NeurIPS 2022 Bayesian inference for aerosol vertical profiles (Papers Track)
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Abstract: Aerosol-cloud interactions constitute the largest source of uncertainty in assessments of the anthropogenic climate change. This uncertainty arises in part from the difficulty in measuring the vertical distributions of aerosols. We often have to settle for less informative vertically aggregated proxies such as aerosol optical depth (AOD). In this work, we develop a framework to infer vertical aerosol profiles using AOD and readily available vertically resolved meteorological predictors such as temperature or relative humidity. We devise a simple Gaussian process prior over aerosol vertical profiles and update it with AOD observations. We validate our approach using ECHAM-HAM aerosol-climate model data. Our results show that, while simple, our model is able to reconstruct realistic extinction profiles with well-calibrated uncertainty. In particular, the model demonstrates a faithful reconstruction of extinction patterns arising from aerosol water uptake in the boundary layer.

Authors: Shahine Bouabid (University of Oxford); Duncan Watson-Parris (University of Oxford); Dino Sejdinovic (University of Adelaide)

Climate Science & Modeling Causal & Bayesian Methods
NeurIPS 2022 Optimizing toward efficiency for SAR image ship detection (Papers Track)
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Abstract: The detection and prevention of illegal fishing is critical to maintaining a healthy and functional ecosystem. Recent research on ship detection in satellite imagery has focused exclusively on performance improvements, disregarding detection efficiency. However, the speed and compute cost of vessel detection are essential for a timely intervention to prevent illegal fishing. Therefore, we investigated optimization methods that lower detection time and cost with minimal performance loss. We trained an object detection model based on a convolutional neural network (CNN) using a dataset of satellite images. Then, we designed two efficiency optimizations that can be applied to the base CNN or any other base model. The optimizations consist of a fast, cheap classification model and a statistical algorithm. The integration of the optimizations with the object detection model leads to a trade-off between speed and performance. We studied the trade-off using metrics that give different weight to execution time and performance. We show that by using a classification model the average precision of the detection model can be approximated to 99.5% in 44% of the time or to 92.7% in 25% of the time.

Authors: Arthur Van Meerbeeck (KULeuven); Ruben Cartuyvels (KULeuven); Jordy Van Landeghem (KULeuven); Sien Moens (KU Leuven)

Computer Vision & Remote Sensing Ecosystems & Biodiversity Oceans & Marine Systems
NeurIPS 2022 AutoML-based Almond Yield Prediction and Projection in California (Papers Track)
Abstract and authors: (click to expand)

Abstract: Almonds are one of the most lucrative products of California, but are also among the most sensitive to climate change. In order to better understand the relationship between climatic factors and almond yield, an automated machine learning framework is used to build a collection of machine learning models. The prediction skill is assessed using historical records. Future projections are derived using 17 downscaled climate outputs. The ensemble mean projection displays almond yield changes under two different climate scenarios, along with two technology development scenarios, where the role of technology development is highlighted. The mean projections and distributions provide insightful results to stakeholders and can be utilized by policymakers for climate adaptation.

Authors: Shiheng Duan (Lawrence Livermore National Laboratory); Shuaiqi Wu (University of California, Davis); Erwan Monier (University of California, Davis); Paul Ullrich (University of California, Davis)

Agriculture & Food
NeurIPS 2022 Attention-Based Scattering Network for Satellite Imagery (Papers Track)
Abstract and authors: (click to expand)

Abstract: Multi-channel satellite imagery, from stacked spectral bands or spatiotemporal data, have meaningful representations for various atmospheric properties. Combining these features in an effective manner to create a performant and trustworthy model is of utmost importance to forecasters. Neural networks show promise, yet suffer from unintuitive computations, fusion of high-level features, and may be limited by the quantity of available data. In this work, we leverage the scattering transform to extract high-level features without additional trainable parameters and introduce a separation scheme to bring attention to independent input channels. Experiments show promising results on estimating tropical cyclone intensity and predicting the occurrence of lightning from satellite imagery.

Authors: Jason Stock (Colorado State University); Charles Anderson (Colorado State University)

Climate Science & Modeling Earth Observation & Monitoring Interpretable ML
NeurIPS 2022 Discovering Interpretable Structural Model Errors in Climate Models (Papers Track)
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Abstract: Inaccuracies in the models of the Earth system, i.e., structural and parametric model errors, lead to inaccurate climate change projections. Errors in the model can originate from unresolved phenomena due to a low numerical resolution, as well as misrepresentations of physical phenomena or boundaries (e.g., orography). Therefore, such models lead to inaccurate short--term forecasts of weather and extreme events, and more importantly, long term climate projections. While calibration methods have been introduced to address for parametric uncertainties, e.g., by better estimation of system parameters from observations, addressing structural uncertainties, especially in an interpretable manner, remains a major challenge. Therefore, with increases in both the amount and frequency of observations of the Earth system, algorithmic innovations are required to identify interpretable representations of the model errors from observations. We introduce a flexible, general-purpose framework to discover interpretable model errors, and show its performance on a canonical prototype of geophysical turbulence, the two--level quasi--geostrophic system. Accordingly, a Bayesian sparsity--promoting regression framework is proposed, that uses a library of kernels for discovery of model errors. As calculating the library from noisy and sparse data (e.g., from observations) using convectional techniques leads to interpolation errors, here we use a coordinate-based multi--layer embedding to impute the sparse observations. We demonstrate the importance of alleviating spectral bias, and propose a random Fourier feature layer to reduce it in the proposed embeddings, and subsequently enable an accurate discovery. Our framework is demonstrated to successfully identify structural model errors due to linear and nonlinear processes (e.g., radiation, surface friction, advection), as well as misrepresented orography.

Authors: Rambod Mojgani (Rice University); Ashesh K Chattopadhyay (Rice University); Pedram Hassanzadeh (Rice University)

Climate Science & Modeling Earth Observation & Monitoring Interpretable ML
NeurIPS 2022 Aboveground carbon biomass estimate with Physics-informed deep network (Papers Track)
Abstract and authors: (click to expand)

Abstract: The global carbon cycle is a key process to understand how our climate is changing. However, monitoring the dynamics is difficult because a high-resolution robust measurement of key state parameters including the aboveground carbon biomass (AGB) is required. We use deep neural network to generate a wall-to-wall map of AGB within the Continental USA (CONUS) with 30-meter spatial resolution for the year 2021. We combine radar and optical hyperspectral imagery, with a physical climate parameter of solar-induced chlorophyll fluorescence (SIF)-based gross primary productivity (GPP). Validation results show that a masked variation of UNet has the lowest validation RMSE of 37.93 ± 1.36 Mg C/ha, as compared to 52.30 ± 0.03 Mg C/ha for random forest algorithm. Furthermore, models that learn from SIF-based GPP in addition to radar and optical imagery reduce validation RMSE by almost 10% and the standard deviation by 40%. Finally, we apply our model to measure losses in AGB from the recent 2021 Caldor wildfire in California, and validate our analysis with Sentinel-based burn index.

Authors: Juan Nathaniel (Columbia University); Levente Klein (IBM Research); Campbell D Watson (IBM Reserch); Gabrielle Nyirjesy (Columbia University); Conrad M Albrecht (IBM Research)

Computer Vision & Remote Sensing Forests Hybrid Physical Models
NeurIPS 2022 Improving the predictions of ML-corrected climate models with novelty detection (Papers Track)
Abstract and authors: (click to expand)

Abstract: While previous works have shown that machine learning (ML) can improve the prediction accuracy of coarse-grid climate models, these ML-augmented methods are more vulnerable to irregular inputs than the traditional physics-based models they rely on. Because ML-predicted corrections feed back into the climate model’s base physics, the ML-corrected model regularly produces out of sample data, which can cause model instability and frequent crashes. This work shows that adding semi-supervised novelty detection to identify out-of-sample data and disable the ML-correction accordingly stabilizes simulations and sharply improves the quality of predictions. We design an augmented climate model with a one-class support vector machine (OCSVM) novelty detector that provides better temperature and precipitation forecasts in a year-long simulation than either a baseline (no-ML) or a standard ML-corrected run. By improving the accuracy of coarse-grid climate models, this work helps make accurate climate models accessible to researchers without massive computational resources.

Authors: Clayton H Sanford (Columbia); Anna Kwa (Allen Institute for Artificial Intelligence); Oliver Watt-Meyer (Allen Institute for AI); Spencer Clark (Allen Institute for AI); Noah Brenowitz (Allen Institute for AI); Jeremy McGibbon (Allen Institute for AI); Christopher Bretherton (Allen Institute for AI)

Climate Science & Modeling Hybrid Physical Models Uncertainty Quantification & Robustness Unsupervised & Semi-Supervised Learning
NeurIPS 2022 Levee protected area detection for improved flood risk assessment in global hydrology models (Papers Track)
Abstract and authors: (click to expand)

Abstract: Precise flood risk assessment is needed to reduce human societies vulnerability as climate change increases hazard risk and exposure related to floods. Levees are built to protect people and goods from flood, which alters river hydrology, but are still not accounted for by global hydrological model. Detecting and integrating levee structures to global hydrological simulations is thus expected to enable more precise flood simulation and risk assessment, with important consequences for flood risk mitigation. In this work, we propose a new formulation to the problem of identifying levee structures: instead of detecting levees themselves, we focus on segmenting the region of the floodplain they protect. This formulation allows to better identify protected areas, to leverage the structure of hydrological data, and to simplify the integration of levee information to global hydrological models.

Authors: Masato Ikegawa (Kobe University); Tristan E.M Hascoet (Kobe University); Victor Pellet (Observatoire de Paris); Xudong Zhou (The University of Tokyo); Tetsuya Takiguchi (Kobe University); Dai Yamazaki (The University of Tokyo)

Disaster Management and Relief
NeurIPS 2022 Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification (Papers Track)
Abstract and authors: (click to expand)

Abstract: Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on fully-annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of LCC. In this study, we propose Scene-to-Patch models: an alternative LCC approach utilising Multiple Instance Learning (MIL) that requires only high-level scene labels. This enables much faster development of new datasets whilst still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using LCC for different scenarios. On the DeepGlobe-LCC dataset, our approach outperforms non-MIL baselines on both scene- and patch-level prediction. This work provides the foundation for expanding the use of LCC in climate change mitigation methods for technology, government, and academia.

Authors: Joseph Early (University of Southampton); Ying-Jung C Deweese (Georgia Insititute of Technology); Christine Evers (University of Southampton); Sarvapali Ramchurn (University of Southampton)

Land Use Earth Observation & Monitoring Agriculture & Food Cities & Urban Planning Forests Interpretable ML Unsupervised & Semi-Supervised Learning
NeurIPS 2022 Deep learning for downscaling tropical cyclone rainfall (Papers Track)
Abstract and authors: (click to expand)

Abstract: Flooding is often the leading cause of mortality and damages from tropical cyclones. With rainfall from tropical cyclones set to rise under global warming, better estimates of extreme rainfall are required to better support resilience efforts. While high resolution climate models capture tropical cyclone statistics well, they are computationally expensive leading to a trade-off between accuracy and generating enough ensemble members to generate sufficient high impact, low probability events. Often, downscaling models are used as a computationally cheaper alternative. Here, we develop and evaluate a set of deep learning models for downscaling tropical cyclone rainfall for more robust risk analysis.

Authors: Emily Vosper (University of Bristol); Lucy Harris (University of Oxford); Andrew McRae (University of Oxford); Laurence Aitchison (University of Bristol); Peter Watson (Bristol); Raul Santos Rodriguez (University of Bristol); Dann Mitchell (University of Bristol)

Extreme Weather Climate Science & Modeling Interpretable ML
NeurIPS 2022 Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes (Papers Track)
Abstract and authors: (click to expand)

Abstract: Short-term forecasting of solar photovoltaic energy (PV) production is important for powerplant management. Ideally these forecasts are equipped with error bars, so that downstream decisions can account for uncertainty. To produce predictions with error bars in this setting, we consider Gaussian processes (GPs) for modelling and predicting solar photovoltaic energy production in the UK. A standard application of GP regression on the PV timeseries data is infeasible due to the large data size and non-Gaussianity of PV readings. However, this is made possible by leveraging recent advances in scalable GP inference, in particular, by using the state-space form of GPs, combined with modern variational inference techniques. The resulting model is not only scalable to large datasets but can also handle continuous data streams via Kalman filtering.

Authors: So Takao (UCL); Sean Nassimiha (UCL); Peter Dudfield (Open Climate Fix); Jack Kelly (Open Climate Fix); Marc Deisenroth (University College London)

Time-series Analysis Climate Science & Modeling Power & Energy Causal & Bayesian Methods Uncertainty Quantification & Robustness
NeurIPS 2022 Identifying latent climate signals using sparse hierarchical Gaussian processes (Papers Track)
Abstract and authors: (click to expand)

Abstract: Extracting latent climate signals from multiple climate model simulations is important to estimate future climate change. To tackle this we develop a sparse hierarchical Gaussian process (SHGP), which probabilistically learns a latent distribution from a set of vectors. We use this to predict the latent surface temperature change globally and for central England from an ensemble of climate models, in a scalable manner and with robust uncertainty propagation.

Authors: Matt Amos (Lancaster University); Thomas Pinder (Lancaster University); Paul Young (Lancaster University)

Climate Science & Modeling Causal & Bayesian Methods Uncertainty Quantification & Robustness
NeurIPS 2022 Towards dynamical stability analysis of sustainable power grids using Graph Neural Networks (Papers Track)
Abstract and authors: (click to expand)

Abstract: To mitigate climate change, the share of renewable needs to be increased. Renewable energies introduce new challenges to power grids due to decentralization, reduced inertia and volatility in production. The operation of sustainable power grids with a high penetration of renewable energies requires new methods to analyze the dynamical stability. We provide new datasets of dynamical stability of synthetic power grids, and find that graph neural networks (GNNs) are surprisingly effective at predicting the highly non-linear target from topological information only. To illustrate the potential to scale to real-sized power grids, we demonstrate the successful prediction on a Texan power grid model.

Authors: Christian Nauck (PIK); Michael Lindner (PIK); Konstantin Schürholt (University of St. Gallen); Frank Hellmann (PIK)

Power & Energy
NeurIPS 2022 Detecting Methane Plumes using PRISMA: Deep Learning Model and Data Augmentation (Papers Track)
Abstract and authors: (click to expand)

Abstract: The new generation of hyperspectral imagers, such as PRISMA, has improved significantly our detection capability of methane (CH4) plumes from space at high spatial resolution (∼30m). We present here a complete framework to identify CH4 plumes using images from the PRISMA satellite mission and a deep learning technique able to automatically detect plumes over large areas. To compensate for the sparse database of PRISMA images, we trained our model by transposing high resolution plumes from Sentinel-2 to PRISMA. Our methodology avoids computationally expensive synthetic plume from Large Eddy Simulations while generating a broad and realistic training database, and paves the way for large-scale detection of methane plumes using future hyperspectral sensors (EnMAP, EMIT, CarbonMapper).

Authors: Alexis Groshenry (Kayrros); Clément Giron (Kayrros); Alexandre d'Aspremont (CNRS, DI, Ecole Normale Supérieure; Kayrros); Thomas Lauvaux (University of Reims Champagne Ardenne, GSMA, UMR 7331); Thibaud Ehret (Centre Borelli)

Computer Vision & Remote Sensing Earth Observation & Monitoring Climate Science & Modeling Meta- and Transfer Learning
NeurIPS 2022 Probabilistic forecasting of regional photovoltaic power production based on satellite-derived cloud motion (Papers Track)
Abstract and authors: (click to expand)

Abstract: Solar energy generation drastically increased in the last years, and it is expected to grow even more in the next decades. So, accurate intra-day forecasts are needed to improve the predictability of the photovoltaic power production and associated balancing measures to increase the shares of renewable energy in the power grid. Most forecasting methods require numerical weather predictions, which are slow to compute, or long-term datasets to run the forecast. These issues make the models difficult to implement in an operational setting. To overcome these problems, we propose a novel regional intraday probabilistic PV power forecasting model able to exploit only 2 hours of satellite-derived cloudiness maps to produce the ensemble forecast. The model is easy to implement in an operational setting as it is based on Pysteps, an already-operational Python library for precipitation nowcasting. With few adaptations of the Steps algorithm, we reached state-of-the-art performance, reaching a 71% lower RMSE than the Persistence model and a 50% lower CRPS than the Persistence Ensemble model for forecast lead times up to 4 hours.

Authors: Alberto Carpentieri (Bern University of Applied Science); Doris Folini (Institute for Atmospheric and Climate Science, ETH Zurich); Martin Wild (Institute for Atmospheric and Climate Science, ETH Zurich); Angela Meyer (Bern University of Applied Science)

Power & Energy Computer Vision & Remote Sensing Uncertainty Quantification & Robustness
NeurIPS 2022 Robustifying machine-learned algorithms for efficient grid operation (Papers Track)
Abstract and authors: (click to expand)

Abstract: We propose a learning-augmented algorithm, RobustML, for operation of dispatchable generation that exploits the good performance of a machine-learned algorithm while providing worst-case guarantees on cost. We evaluate the algorithm on a realistic two-generator system, where it exhibits robustness to distribution shift while enabling improved efficiency as renewable penetration increases.

Authors: Nicolas Christianson (California Institute of Technology); Christopher Yeh (California Institute of Technology); Tongxin Li (The Chinese University of Hong Kong (Shenzhen)); Mahdi Torabi Rad (Beyond Limits); Azarang Golmohammadi (Beyond Limits, Inc.); Adam Wierman (California Institute of Technology)

Uncertainty Quantification & Robustness Power & Energy Reinforcement Learning
NeurIPS 2022 Deep Hydrology: Hourly, Gap-Free Flood Maps Through Joint Satellite and Hydrologic Modelling (Papers Track)
Abstract and authors: (click to expand)

Abstract: Climate change-driven weather disasters are rapidly increasing in both frequency and magnitude. Floods are the most damaging of these disasters, with approximately 1.46 billion people exposed to inundation depths of over 0.15m, a significant life and livelihood risk. Accurate knowledge of flood-extent for ongoing and historical events facilitates climate adaptation in flood-prone communities by enabling near real-time disaster monitoring to support planning, response, and relief during these extreme events. Satellite observations can be used to derive flood-extent maps directly; however, these observations are impeded by cloud and canopy cover, and can be very infrequent and hence miss the flood completely. In contrast, physically-based inundation models can produce spatially complete event maps but suffer from high uncertainty if not frequently calibrated with expensive land and infrastructure surveys. In this study, we propose a deep learning approach to reproduce satellite-observed fractional flood-extent maps given dynamic state variables from hydrologic models, fusing information contained within the states with direct observations from satellites. Our model has an hourly temporal resolution, contains no cloud-gaps, and generalizes to watersheds across the continental United States with a 6% error on held-out areas that never flooded before. We further demonstrate through a case study in Houston, Texas that our model can distinguish tropical cyclones that caused flooding from those that did not within two days of landfall, thereby providing a reliable source for flood-extent maps that can be used by disaster monitoring services.

Authors: Tanya Nair (Cloud To Street); Veda Sunkara (Cloud to Street); Jonathan Frame (Cloud to Street); Philip Popien (Cloud to Street); Subit Chakrabarti (Cloud To Street)

Computer Vision & Remote Sensing Climate Justice Climate Science & Modeling Disaster Management and Relief Extreme Weather Hybrid Physical Models
NeurIPS 2022 Machine learning emulation of a local-scale UK climate model (Papers Track)
Abstract and authors: (click to expand)

Abstract: Climate change is causing the intensification of rainfall extremes. Precipitation projections with high spatial resolution are important for society to prepare for these changes, e.g. to model flooding impacts. Physics-based simulations for creating such projections are very computationally expensive. This work demonstrates the effectiveness of diffusion models, a form of deep generative models, for generating much more cheaply realistic high resolution rainfall samples for the UK conditioned on data from a low resolution simulation. We show for the first time a machine learning model that is able to produce realistic high-resolution rainfall predictions based on a physical model that resolves atmospheric convection, a key process behind extreme rainfall. By adding self-learnt, location-specific information to low resolution relative vorticity, quantiles and time-mean of the samples match well their counterparts from the high-resolution simulation.

Authors: Henry Addison (University of Bristol); Elizabeth Kendon (Met Office Hadley Centre); Suman Ravuri (DeepMind); Laurence Aitchison (University of Bristol); Peter Watson (Bristol)

Climate Science & Modeling Extreme Weather Generative Modeling
NeurIPS 2022 Bridging the Microwave Data Gap; Using Bayesian Deep Learning to “See” the Unseen (Papers Track)
Abstract and authors: (click to expand)

Abstract: Having microwave data with the spatial and temporal resolution of infrared data would provide a large positive impact on many climate and weather applications. We demonstrate that Bayesian deep learning is a promising technique for both creating and improving synthetic microwave data from infrared data. We report 0.7% mean absolute percentage error for 183+/-3 GHz microwave brightness temperature and uncertainty metrics and find that more training data is needed to achieve improved performance at 166 GHz, 37 GHz, and 23 GHz. Analysis of the spatial distribution of uncertainty reveals that additional cloud data will provide the greatest increase in skill, which will potentially allow for generation of many secondary products derived from microwave data in the future.

Authors: Pedro Ortiz (Naval Postgraduate School); Eleanor Casas (Naval Postgraduate School); Marko Orescanin (Naval Postgraduate School); Scott Powell (Naval Postgraduate School)

Earth Observation & Monitoring Climate Science & Modeling Extreme Weather Causal & Bayesian Methods Uncertainty Quantification & Robustness
NeurIPS 2022 Learning evapotranspiration dataset corrections from water cycle closure supervision (Papers Track)
Abstract and authors: (click to expand)

Abstract: Evapotranspiration (ET) is one of the most uncertain components of the global water cycle. Improving global ET estimates is needed to better our understanding of the global water cycle so as to forecast the consequences of climate change on the future of global water resource distribution. This work presents a methodology to derive monthly corrections of global ET datasets at 0.25 degree resolution. We use ML to generalize sparse catchment-level water cycle closure residual information to global and dense pixel-level residuals. Our model takes a probabilistic view on ET datasets and their correction that we use to regress catchment-level residuals using a sum-aggregated supervision. Using four global ET datasets, we show that our learned model has learned ET corrections that accurately generalize its water cycle-closure results to unseen catchments.

Authors: Tristan E.M Hascoet (Kobe University); Victor Pellet (LERMA); Filipe Aires (LERMA)

Earth Observation & Monitoring Climate Science & Modeling Causal & Bayesian Methods
NeurIPS 2022 Convolutional Neural Processes for Inpainting Satellite Images: Application to Water Body Segmentation (Papers Track)
Abstract and authors: (click to expand)

Abstract: The widespread availability of satellite images has allowed researchers to monitor the impact of climate on socio-economic and environmental issues through examples like crop and water body classification to measure food scarcity and risk of flooding. However, a common issue of satellite images is missing values due to measurement defects, which render them unusable by existing methods without data imputation. To repair the data, inpainting methods can be employed, which are based on classical PDEs or interpolation methods. Recently, deep learning approaches have shown promise in this realm, however many of these methods do not explicitly take into account the inherent spatio-temporal structure of satellite images. In this work, we cast satellite image inpainting as a meta-learning problem, and implement Convolutional Neural Processes (ConvNPs) in which we frame each satellite image as its own task or 2D regression problem. We show that ConvNPs outperform classical methods and state-of-the-art deep learning inpainting models on a scanline problem for LANDSAT 7 satellite images, assessed on a variety of in- and out-of-distribution images. Our results successfully match the performance of clean images on a downstream water body segmentation task in Canada.

Authors: Alexander Pondaven (Imperial College London); Mart Bakler (Imperial College London); Donghu Guo (Imperial College London); Hamzah Hashim (Imperial College London); Martin G Ignatov (Imperial college London); Samir Bhatt (Imperial College London); Seth Flaxman (Oxford); Swapnil Mishra (Imperial College London); Elie Alhajjar (USMA); Harrison Zhu (Imperial College London)

Computer Vision & Remote Sensing Earth Observation & Monitoring Generative Modeling Meta- and Transfer Learning
NeurIPS 2022 A POMDP Model for Safe Geological Carbon Sequestration (Papers Track)
Abstract and authors: (click to expand)

Abstract: Geological carbon capture and sequestration (CCS), where CO2 is stored in subsurface formations, is a promising and scalable approach for reducing global emissions.However, if done incorrectly, it may lead to earthquakes and leakage of CO2 back to the surface, harming both humans and the environment. These risks are exacerbated by the large amount of uncertainty in the structure of the storage formation. For these reasons, we propose that CCS operations be modeled as a partially observable Markov decision process (POMDP) and decisions be informed using automated planning algorithms. To this end, we develop a simplified model of CCS operations based on a 2D spillpoint analysis that retains many of the challenges and safety considerations of the real-world problem. We show how off-the-shelf POMDP solvers outperform expert baselines for safe CCS planning. This POMDP model can be used as a test bed to drive the development of novel decision-making algorithms for CCS operations.

Authors: Anthony Corso (Stanford University); Yizheng Wang (Stanford Univerity); Markus Zechner (Stanford University); Jef Caers (Stanford University); Mykel J Kochenderfer (Stanford University)

Carbon Capture & Sequestration Reinforcement Learning
NeurIPS 2022 Optimizing Japanese dam reservoir inflow forecast for efficient operation (Papers Track)
Abstract and authors: (click to expand)

Abstract: Despite a climate and topology favorable to hydropower (HP) generation, HP only accounts for 4% of today’s Japanese primary energy consumption mix. In recent years, calls for improving the efficiency of Japanese HP towards achieving a more sustainable energy mix have emerged from prominent voices in the Ministry of Land, Infrastructure, Transport and Tourism (MILT). Among potential optimizations, data-driven dam operation policies using accurate river discharge forecasts have been advocated for. In the meantime, Machine Learning (ML) has recently made important strides in hydrological modeling, with forecast accuracy improvements demonstrated on both precipitation nowcasting and river discharge prediction. We are motivated by the convergence of these societal and technological contexts: our final goal is to provide scientific evidence and actionable insights for dam infrastructure managers and policy makers to implement more energy-efficient and flood-resistant dam operation policies on a national scale. Towards this goal this work presents a preliminary study of ML-based dam inflow forecasts on a dataset of 127 Japanese public dams we assembled. We discuss our preliminary results and lay out a path for future studies.

Authors: Keisuke Yoshimi (Kobe University); Tristan E.M Hascoet (Kobe University); Rousslan F. Julien Dossa (Kobe University); Ryoichi Takashima (Kobe University); Tetsuya Takiguchi (Kobe University); Satoru Oishi (Kobe University)

Climate Science & Modeling Time-series Analysis
NeurIPS 2022 Deep Climate Change: A Dataset and Adaptive domain pre-trained Language Models for Climate Change Related Tasks (Papers Track)
Abstract and authors: (click to expand)

Abstract: The quantity and quality of literature around climate change (CC) and its impacts are increasing yearly. Yet, this field has received limited attention in the Natural Language Processing (NLP) community. With the help of large Language Models (LMs) and transfer learning, NLP can support policymakers, researchers, and climate activists in making sense of large-scale and complex CC-related texts. CC-related texts include specific language that general language models cannot represent accurately. Therefore we collected a climate change corpus consisting of over 360 thousand abstracts of top climate scientists' articles from trustable sources covering large temporal and spatial scales. Comparison of the performance of GPT2 LM and our 'climateGPT2 models', fine-tuned on the CC-related corpus, on claim generation (text generation) and fact-checking, downstream tasks show the better performance of the climateGPT2 models compared to the GPT2. The climateGPT2 models decrease the validation loss to 1.08 for claim generation from 43.4 obtained by GPT2. We found that climateGPT2 models improved the masked language model objective for the fact-checking task by increasing the F1 score from 0.67 to 0.72.

Authors: Saeid Vaghefi (University of Zürich); Veruska Muccione (University of Zürich); Christian Huggel (University of Zürich); Hamed Khashehchi (2w2e GmbH); Markus Leippold (University of Zurich)

Natural Language Processing Meta- and Transfer Learning
NeurIPS 2022 Data-Driven Optimal Solver for Coordinating a Sustainable and Stable Power Grid (Papers Track)
Abstract and authors: (click to expand)

Abstract: With today's pressing climate change concerns, the widespread integration of low-carbon technologies such as sustainable generation systems (e.g. photovoltaics, wind turbines, etc.) and flexible consumer devices (e.g. storage, electric vehicles, smart appliances, etc.) into the electric grid is vital. Although these power entities can be deployed at large, these are highly variable in nature and must interact with the existing grid infrastructure without violating electrical limits so that the system continues to operate in a stable manner at all times. In order to ensure the integrity of grid operations while also being economical, system operators will need to rapidly solve the optimal power flow (OPF) problem in order to adapt to these fluctuations. Inherent non-convexities in the OPF problem do not allow traditional model-based optimization techniques to offer guarantees on optimality, feasibility and convergence. In this paper, we propose a data-driven OPF solver built on information-theoretic and semi-supervised machine learning constructs. We show that this solver is able to rapidly compute solutions (i.e. in sub-second range) that are within 3\% of optimality with guarantees on feasibility on a benchmark IEEE 118-bus system.

Authors: Junfei Wang (York University); Pirathayini Srikantha (York University)

Power & Energy Climate Finance & Economics Unsupervised & Semi-Supervised Learning
NeurIPS 2022 Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation (Papers Track)
Abstract and authors: (click to expand)

Abstract: How can we learn from all available data when training machine-learnt climate models, without incurring any extra cost at simulation time? Typically, the training data comprises coarse-grained high-resolution data. But only keeping this coarse-grained data means the rest of the high-resolution data is thrown out. We use a transfer learning approach, which can be applied to a range of machine learning models, to leverage all the high-resolution data. We use three chaotic systems to show it stabilises training, gives improved generalisation performance and results in better forecasting skill. Our code is at https://github.com/raghul-parthipan/dont_waste_data

Authors: Raghul Parthipan (University of Cambridge); Damon Wischik (Univeristy of Cambridge)

Climate Science & Modeling Generative Modeling Meta- and Transfer Learning Time-series Analysis
NeurIPS 2022 Explainable Multi-Agent Recommendation System for Energy-Efficient Decision Support in Smart Homes (Papers Track)
Abstract and authors: (click to expand)

Abstract: Transparent, understandable, and persuasive recommendations support the electricity consumers’ behavioral change to tackle the energy efficiency problem. This paper proposes an explainable multi-agent recommendation system for load shifting for household appliances. First, we extend a novel multi-agent approach by designing an Explainability Agent that provides explainable recommendations for optimal appliance scheduling in a textual and visual manner. Second, we enhance the predictive capacity of other agents by including weather data and applying state-of-the-art models (i.e., k-nearest-neighbours, extreme gradient boosting, adaptive boosting, random forest, logistic regression, and explainable boosting machines). Since we want to help the user understand a single recommendation, we focus on local explainability approaches. In particular, we apply post-model approaches LIME (local, interpretable, model-agnostic explanation) and SHAP (Shapley additive explanations) as model-agnostic tools that can explain the predictions of the chosen classifiers. We further provide an overview of the predictive and explainability performance. Our results show a substantial improvement in the performance of the multi-agent system while at the same time opening up the “black box” of recommendations. To show the pathway to positive impact regarding climate change, we provide a discussion on the potential impact of the suggested approach.

Authors: Alona Zharova (Humboldt University of Berlin); Annika Boer (Humboldt University of Berlin); Julia Knoblauch (Humboldt University of Berlin); Kai Ingo Schewina (Humboldt University of Berlin); Jana Vihs (Humboldt University of Berlin)

Recommender Systems Buildings Power & Energy Interpretable ML
NeurIPS 2022 FIRO: A Deep-neural Network for Wildfire Forecast with Interpretable Hidden States (Papers Track)
Abstract and authors: (click to expand)

Abstract: Several wildfire danger systems have emerged from decades of research. One such system is the National Fire-Danger Rating System (NFDRS), which is used widely across the United States and is a key predictor in the Global ECMWF Fire Forecasting (GEFF) model. The NFDRS is composed of over 100 equations relating wildfire risk to weather conditions, climate and land cover characteristics, and fuel. These equations and the corresponding 130+ parameters were developed via field and lab experiments. These parameters, which are fixed in the standard NFDRS and GEFF implementations, may not be the most appropriate for a climate-changing world. In order to adjust the NFDRS parameters to current climate conditions and specific geographical locations, we recast NFDRS in PyTorch to create a new deep learning-based Fire Index Risk Optimizer (FIRO). FIRO predicts the ignition component, or the probability a wildfire would require suppression in the presence of a firebrand, and calibrates the uncertain parameters for a specific region and climate conditions by training on observed fires. Given the rare occurrence of wildfires, we employed the extremal dependency index (EDI) as the loss function. Using ERA5 reanalysis and MODIS burned area data, we trained FIRO models for California, Texas, Italy, and Madagascar. Across these four geographies, the average EDI improvement was 175% above the standard NFDRS implementation

Authors: Eduardo R Rodrigues (MSR); Campbell D Watson (IBM Reserch); Bianca Zadrozny (IBM Research); Gabrielle Nyirjesy (Columbia University)

Disaster Management and Relief Forests Interpretable ML
NeurIPS 2022 Towards a spatially transferable super resolution model for downscaling Antarctic surface melt (Papers Track)
Abstract and authors: (click to expand)

Abstract: Surface melt on the Antarctic Ice Sheet is an important climate indicator, yet the spatial scale of modeling and observing surface melt is insufficient to capture crucial details and understand local processes. High-resolution climate models could provide a solution, but they are computationally expensive and require finetuning for some model parameters. An alternative method, pioneering in geophysics, is single-image super resolution (SR) applied on lower-resolution model output. However, often input and output of such SR models are available on the same, fixed spatial domain. High-resolution model simulations over Antarctica are available only in some regions. To be able to apply an SR model elsewhere, we propose to make the single-image SR model physics-aware, using surface albedo and elevation as additional input. Our results show a great improvement in the spatial transferability of the conventional SR model. Although issues with the input satellite-derived albedo remain, adding physics awareness paves a way toward a spatially transferable SR model for downscaling Antarctic surface melt.

Authors: Zhongyang Hu (IMAU); Yao Sun (TUM); Peter Kuipers Munneke (IMAU); Stef Lhermitte (TU Delft); Xiaoxiang Zhu (Technical University of Munich,Germany)

Climate Science & Modeling Computer Vision & Remote Sensing
NeurIPS 2022 Forecasting European Ozone Air Pollution With Transformers (Papers Track)
Abstract and authors: (click to expand)

Abstract: Surface ozone is an air pollutant that contributes to hundreds of thousands of premature deaths annually. Accurate short-term ozone forecasts may allow improved policy to reduce the risk to health, such as air quality warnings. However, forecasting ozone is a difficult problem, as surface ozone concentrations are controlled by a number of physical and chemical processes which act on varying timescales. Accounting for these temporal dependencies appropriately is likely to provide more accurate ozone forecasts. We therefore deploy a state-of-the-art transformer-based model, the Temporal Fusion Transformer, trained on observational station data from three European countries. In four-day test forecasts of daily maximum 8-hour ozone, the novel approach is highly skilful (MAE = 4.6 ppb, R2 = 0.82), and generalises well to two European countries unseen during training (MAE = 4.9 ppb, R2 = 0.79). The model outperforms standard machine learning models on our data, and compares favourably to the published performance of other deep learning architectures tested on different data. We illustrate that the model pays attention to physical variables known to control ozone concentrations, and that the attention mechanism allows the model to use relevant days of past ozone concentrations to make accurate forecasts.

Authors: Seb Hickman (University of Cambridge); Paul Griffiths (University of Cambridge); Alex Archibald (University of Cambridge); Peer Nowack (Imperial College London); Elie Alhajjar (USMA)

Climate Science & Modeling Extreme Weather Health Public Policy Societal Adaptation & Resilience Interpretable ML Time-series Analysis
NeurIPS 2022 Stability Constrained Reinforcement Learning for Real-Time Voltage Control (Papers Track)
Abstract and authors: (click to expand)

Abstract: This paper is a summary of a recently submitted work. Deep Reinforcement Learning (DRL) has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world power systems has been hindered by a lack of explicit stability and safety guarantees. In this paper, we propose a stability constrained reinforcement learning method for real-time voltage control in both single-phase and three-phase distribution grids. The key idea underlying our approach is an explicitly constructed Lyapunov function that certifies stability. We demonstrate the effectiveness of our approach with IEEE test feeders, where the proposed method achieves the best overall performance, while always achieving voltage stability. In contrast, standard RL methods often fail to achieve voltage stability.

Authors: Jie Feng (UCSD); Yuanyuan Shi (University of California San Diego); Guannan Qu (Carnegie Mellon University); Steven Low (California Institute of Technology); Animashree Anandkumar (Caltech); Adam Wierman (California Institute of Technology)

Power & Energy Reinforcement Learning
NeurIPS 2022 Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning (Papers Track)
Abstract and authors: (click to expand)

Abstract: Satellite image analysis has important implications for land use, urbanization, and ecosystem monitoring. Deep learning methods can facilitate the analysis of different satellite modalities, such as electro-optical (EO) and synthetic aperture radar (SAR) imagery, by supporting knowledge transfer between the modalities to compensate for individual shortcomings. Recent progress has shown how distributional alignment of neural network embeddings can produce powerful transfer learning models by employing a sliced Wasserstein distance (SWD) loss. We analyze how this method can be applied to Sentinel-1 and -2 satellite imagery and develop several extensions toward making it effective in practice. In an application to few-shot Local Climate Zone (LCZ) prediction, we show that these networks outperform multiple common baselines on datasets with a large number of classes. Further, we provide evidence that instance normalization can significantly stabilize the training process and that explicitly shaping the embedding space using supervised contrastive learning can lead to improved performance.

Authors: Marcel Hussing (University of Pennsylvania); Karen Li (University of Pennsylvania); Eric Eaton (University of Pennsylvania)

Land Use Meta- and Transfer Learning Cities & Urban Planning Earth Observation & Monitoring
NeurIPS 2022 Exploring Randomly Wired Neural Networks for Climate Model Emulation (Papers Track)
Abstract and authors: (click to expand)

Abstract: Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth system models can provide detailed insight into these impacts, but have a large associated computational cost on a per-scenario basis. This large computational burden has driven recent interest in developing cheap machine learning models for the task of climate model emulation. In this manuscript, we explore the efficacy of randomly wired neural networks for this task. We describe how they can be constructed and compare them to their standard feedforward counterparts using the ClimateBench dataset. Specifically, we replace the dense layers in multilayer perceptrons, convolutional neural networks, and convolutional long short-term memory networks with randomly wired ones and assess the impact on model performance for models with 1 million and 10 million parameters. We find average performance improvements of 4.2% across model complexities and prediction tasks, with substantial performance improvements of up to 16.4% in some cases. Furthermore, we find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers. These findings indicate that randomly wired neural networks may be suitable direct replacements for traditional dense layers in many standard models.

Authors: William J Yik (Harvey Mudd College); Sam J Silva (The University of Southern California); Andrew Geiss (Pacific Northwest National Laboratory); Duncan Watson-Parris (University of Oxford)

Climate Science & Modeling Earth Observation & Monitoring Extreme Weather Computer Vision & Remote Sensing Time-series Analysis
NeurIPS 2022 Closing the Domain Gap -- Blended Synthetic Imagery for Climate Object Detection (Papers Track)
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Abstract: Object detection models have great potential to increase both the frequency and cost-efficiency of assessing climate-relevant infrastructure in satellite imagery. However, model performance can suffer when models are applied to stylistically different geographies. We propose a technique to generate synthetic imagery using minimal labeled examples of the target object at a low computational cost. Our technique blends example objects onto unlabeled images of the target domain. We show that including these synthetic images improves the average precision of a YOLOv3 object detection model when compared to a baseline and other popular domain adaptation techniques.

Authors: Caleb Kornfein (Duke University); Frank Willard (Duke University); Caroline Tang (Duke University); Yuxi Long (Duke University); Saksham Jain (Duke University); Jordan Malof (Duke University); Simiao Ren (Duke University); Kyle Bradbury (Duke University)

Computer Vision & Remote Sensing Meta- and Transfer Learning
NeurIPS 2022 SustainGym: A Benchmark Suite of Reinforcement Learning for Sustainability Applications (Papers Track)
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Abstract: The lack of standardized benchmarks for reinforcement learning (RL) in sustainability applications has made it difficult to both track progress on specific domains and identify bottlenecks for researchers to focus their efforts on. In this paper, we present SustainGym, a suite of two environments designed to test the performance of RL algorithms on realistic sustainability tasks. The first environment simulates the problem of scheduling decisions for a fleet of electric vehicle (EV) charging stations, and the second environment simulates decisions for a battery storage system bidding in an electricity market. We describe the structure and features of the environments and show that standard RL algorithms have significant room for improving performance. We discuss current challenges in introducing RL to real-world sustainability tasks, including physical constraints and distribution shift.

Authors: Christopher Yeh (California Institute of Technology); Victor Li (California Institute of Technology); Rajeev Datta (California Institute of Technology); Yisong Yue (Caltech); Adam Wierman (California Institute of Technology)

Reinforcement Learning Power & Energy
NeurIPS 2022 Remote estimation of geologic composition using interferometric synthetic-aperture radar in California’s Central Valley (Papers Track)
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Abstract: California's Central Valley is the national agricultural center, producing 1/4 of the nation’s food. However, land in the Central Valley is sinking at a rapid rate (as much as 20 cm per year) due to continued groundwater pumping. Land subsidence has a significant impact on infrastructure resilience and groundwater sustainability. In this study, we aim to identify specific regions with different temporal dynamics of land displacement and find relationships with underlying geological composition. Then, we aim to remotely estimate geologic composition using interferometric synthetic aperture radar (InSAR)-based land deformation temporal changes using machine learning techniques. We identified regions with different temporal characteristics of land displacement in that some areas (e.g., Helm) with coarser grain geologic compositions exhibited potentially reversible land deformation (elastic land compaction). We found a significant correlation between InSAR-based land deformation and geologic composition using random forest and deep neural network regression models. We also achieved significant accuracy with 1/4 sparse sampling to reduce any spatial correlations among data, suggesting that the model has the potential to be generalized to other regions for indirect estimation of geologic composition. Our results indicate that geologic composition can be estimated using InSAR-based land deformation data. In-situ measurements of geologic composition can be expensive and time consuming and may be impractical in some areas. The generalizability of the model sheds light on high spatial resolution geologic composition estimation utilizing existing measurements.

Authors: Kyongsik Yun (California Institute of Technology); Kyra Adams (California Institute of Technology); John Reager (California Institute of Technology); Zhen Liu (California Institute of Technology); Caitlyn Chavez (California Institute of Technology); Michael Turmon (California Institute of Technology); Thomas Lu (California Institute of Technology)

Earth Observation & Monitoring Interpretable ML
NeurIPS 2022 AutoML for Climate Change: A Call to Action (Papers Track)
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Abstract: The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications. The climate change ML (CCML) community works on a diverse, challenging set of problems which often involve physics-constrained ML or heterogeneous spatiotemporal data. It would be desirable to use automated machine learning (AutoML) techniques to automatically find high-performing architectures and hyperparameters for a given dataset. In this work, we benchmark popular Auto ML libraries on three high-leverage CCML applications: climate modeling, wind power forecasting, and catalyst discovery. We find that out-of-the-box AutoML libraries currently fail to meaningfully surpass the performance of human-designed CCML models. However, we also identify a few key weaknesses, which stem from the fact that most AutoML techniques are tailored to computer vision and NLP applications. For example, while dozens of search spaces have been designed for image and language data, none have been designed for spatiotemporal data. Addressing these key weaknesses can lead to the discovery of novel architectures that yield substantial performance gains across numerous CCML applications. Therefore, we present a call to action to the AutoML community, since there are a number of concrete, promising directions for future work in the space of AutoML for CCML. We release our code and a list of resources at https://github.com/climate-change-automl/climate-change-automl.

Authors: Renbo Tu (University of Toronto); Nicholas Roberts (University of Wisconsin-Madison); Vishak Prasad C (Indian Institute Of Technology, Bombay); Sibasis Nayak (Indian Institute of Technology, Bombay); Paarth Jain (Indian Institute of Technology Bombay); Frederic Sala (University of Wisconsin-Madison); Ganesh Ramakrishnan (IIT Bombay); Ameet Talwalkar (CMU); Willie Neiswanger (Stanford University); Colin White (Abacus.AI)

Climate Science & Modeling Chemistry & Materials Power & Energy
NeurIPS 2022 Temperature impacts on hate speech online: evidence from four billion tweets (Papers Track)
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Abstract: Human aggression is no longer limited to the physical space but exists in the form of hate speech on social media. Here, we examine the effect of temperature on the occurrence of hate speech on Twitter and interpret the results in the context of climate change, human behavior and mental health. Employing supervised machine learning models, we identify hate speech in a data set of four billion geolocated tweets from over 750 US cities (2014 – 2020). We statistically evaluate the changes in daily hate tweets against changes in local temperature, isolating the temperature influence from confounding factors using binned panel-regression models. We find a low prevalence of hate tweets in moderate temperatures and observe sharp increases of up to 12% for colder and up to 22% for hotter temperatures, indicating that not only hot but also cold temperatures increase aggressive tendencies. Further, we observe that for extreme temperatures hate speech also increases as a percentage of total tweeting activity, crowding out non-hate speech. The quasi-quadratic shape of the temperature-hate tweet curve is robust across varying climate zones, income groups, religious and political beliefs. The prevalence of the results across climatic and socioeconomic splits points to limits in adaptation. Our results illuminate hate speech online as an impact channel through which temperature alters societal aggression.

Authors: Annika Stechemesser (Potsdam Insitute for Climate Impact Research); Anders Levermann (Potsdam Institute for Climate Impact Research); Leonie Wenz (Potsdam Institute for Climate Impact Research)

Health Societal Adaptation & Resilience Data Mining Natural Language Processing
NeurIPS 2022 Cross Modal Distillation for Flood Extent Mapping (Papers Track)
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Abstract: The increasing intensity and frequency of floods is one of the many consequences of our changing climate. In this work, we explore ML techniques that improve the flood detection module of an operational early flood warning system. Our method exploits an unlabelled dataset of paired multi-spectral and Synthetic Aperture Radar (SAR) imagery to reduce the labeling requirements of a purely supervised learning method. Past attempts have used such unlabelled data by creating weak labels out of them, but end up learning the label mistakes in those weak labels. Motivated by knowledge distillation and semi supervised learning, we explore the use of a teacher to train a student with the help of a small hand labeled dataset and a large unlabelled dataset. Unlike the conventional self distillation setup, we propose a cross modal distillation framework that transfers supervision from a teacher trained on richer modality (multi-spectral images) to a student model trained on SAR imagery. The trained models are then tested on the Sen1Floods11 dataset. Our model outperforms the Sen1Floods11 SAR baselines by an absolute margin of 4.15% pixel wise Intersection-over-Union (IoU) on the test split.

Authors: Shubhika Garg (Google); Ben Feinstein (Google); Shahar Timnat (Google); Vishal V Batchu (Google); gideon dror (The Academic College of Tel-Aviv-Yaffo); Adi Gerzi Rosenthal (Google); Varun Gulshan (Google Research)

Computer Vision & Remote Sensing Disaster Management and Relief Earth Observation & Monitoring Unsupervised & Semi-Supervised Learning
NeurIPS 2022 Transformer Neural Networks for Building Load Forecasting (Papers Track)
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Abstract: Accurate electrical load forecasts of buildings are needed to optimize local energy storage and to make use of demand-side flexibility. We study the usage of Transformer neural networks for short-term electrical load forecasting of 296 buildings from a public dataset. Transformer neural networks trained on many buildings give the best forecasts on 115 buildings, and multi-layer perceptrons trained on a single building are better on 161 buildings. In addition, we evaluate the models on buildings that were not used for training, and find that Transformer neural networks generalize better than multi-layer perceptrons and our statistical baselines. This shows that the usage of Transformer neural networks for building load forecasting could reduce training resources due to the good generalization to unseen buildings, and they could be useful for cold-start scenarios.

Authors: Matthias Hertel (KIT); Simon Ott (KIT); Oliver Neumann (KIT); Benjamin Schäfer (KIT); Ralf Mikut (Karlsruhe Institute of Technology); Veit Hagenmeyer (Karlsruhe Institute of Technology (KIT))

Time-series Analysis Buildings
NeurIPS 2022 Estimating Chicago’s tree cover and canopy height using multi-spectral satellite imagery (Papers Track)
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Abstract: Information on urban tree canopies is fundamental to mitigating climate change as well as improving quality of life. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-truth and then trains a multi-task machine learning model to generate reliable estimates of tree cover and canopy height in urban areas using multi-source multi-spectral satellite imagery for the case study of Chicago.

Authors: John Francis (University College London)

Cities & Urban Planning Ecosystems & Biodiversity Public Policy Computer Vision & Remote Sensing
NeurIPS 2022 Reconstruction of Grid Measurements in the Presence of Adversarial Attacks (Papers Track)
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Abstract: In efforts to mitigate the adverse effects of climate change, policymakers have set ambitious goals to reduce the carbon footprint of all sectors - including the electric grid. To facilitate this, sustainable energy systems like renewable generation must { be} deployed at high numbers throughout the grid. As these are highly variable in nature, the grid must be closely monitored so that system operators will have elevated situational awareness and can execute timely actions to maintain stable grid operations. With the widespread deployment of sensors like phasor measurement units (PMUs), an abundance of data is available for conducting accurate state estimation. However, due to the cyber-physical nature of the power grid, measurement data can be perturbed in an adversarial manner to enforce incorrect decision-making. In this paper, we propose a novel reconstruction method that leverages on machine learning constructs like CGAN and gradient search to recover the original states when subjected to adversarial perturbations. Experimental studies conducted on the practical IEEE 118-bus benchmark power system show that the proposed method can reduce errors due to perturbation by large margins (i.e. up to 100%).

Authors: Amirmohammad Naeini (York University); Samer El Kababji (Western University); Pirathayini Srikantha (York University)

Generative Modeling Unsupervised & Semi-Supervised Learning
NeurIPS 2022 Heat Demand Forecasting with Multi-Resolutional Representation of Heterogeneous Temporal Ensemble (Papers Track)
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Abstract: One of the primal challenges faced by utility companies is ensuring efficient supply with minimal greenhouse gas emissions. The advent of smart meters and smart grids provide an unprecedented advantage in realizing an optimised supply of thermal energies through proactive techniques such as load forecasting. In this paper, we propose a forecasting framework for heat demand based on neural networks where the time series are encoded as scalograms equipped with the capacity of embedding exogenous variables such as weather, and holiday/non-holiday. Subsequently, CNNs are utilized to predict the heat load multi-step ahead. Finally, the proposed framework is compared with other state-of-the-art methods, such as SARIMAX and LSTM. The quantitative results from retrospective experiments show that the proposed framework consistently outperforms the state-of-the-art baseline method with real-world data acquired from Denmark. A minimal mean error of 7.54% for MAPE and 417kW for RMSE is achieved with the proposed framework in comparison to all other methods.

Authors: Satyaki Chatterjee (Pattern Recognition Lab, FAU Erlangen-Nuremberg); Adithya Ramachandran (Pattern Recognition Lab, Friedrich Alexander University, Erlangen); Thorkil Flensmark Neergaard (Brønderslev Forsyning A/S); Andreas K Maier (Pattern Recognition Lab, FAU Erlangen-Nuremberg); Siming Bayer (Pattern Recognition Lab, Friedrich-Alexander University)

Time-series Analysis Cities & Urban Planning Power & Energy Interpretable ML
NeurIPS 2022 Generalized Ice Detection on Wind Turbine Rotor Blades with Neural Style Transfer (Papers Track)
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Abstract: Wind energy’s ability to liberate the world of conventional sources of energy relies on lowering the significant costs associated with the maintenance of wind turbines. Since icing events on turbine rotor blades are a leading cause of operational failures, identifying icing in advance is critical. Some recent studies focus on specific wind parks and fail to generalize to unseen scenarios (e.g. new rotor blade designs). We propose the utilisation of synthetic data augmentation via neural style transfer to improve the generalization of existing ice prediction models. We show that training models with augmented data that captures domain-invariant icing characteristics can help improve predictive performance across multiple wind parks. Through efficient identification of icing, this study can support preventive maintenance of wind energy sources by making them more reliable towards tackling climate change.

Authors: Joyjit Chatterjee (University of Hull); Maria Teresa Alvela Nieto (University of Bremen); Hannes Gelbhardt (University of Bremen); Nina Dethlefs (University of Hull); Jan Ohlendorf (University of Bremen); Klaus-Dieter Thoben (University of Bremen)

Power & Energy Computer Vision & Remote Sensing Meta- and Transfer Learning
NeurIPS 2022 Identifying Compound Climate Drivers of Forest Mortality with β-VAE (Papers Track)
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Abstract: Climate change is expected to lead to higher rates of forest mortality. Forest mortality is a complex phenomenon driven by the interaction of multiple climatic variables at multiple temporal scales, further modulated by the current state of the forest (e.g. age, stem diameter, and leaf area index). Identifying the compound climate drivers of forest mortality would greatly improve understanding and projections of future forest mortality risk. Observation data are, however, limited in accuracy and sample size, particularly in regard to forest state variables and mortality events. In contrast, simulations with state-of-the-art forest models enable the exploration of novel machine learning techniques for associating forest mortality with driving climate conditions. Here we simulate 160,000 years of beech, pine and spruce forest dynamics with the forest model FORMIND. We then apply β-VAE to learn disentangled latent representations of weather conditions and identify those that are most likely to cause high forest mortality. The learned model successfully identifies three characteristic climate representations that can be interpreted as different compound drivers of forest mortality.

Authors: Mohit Anand (Helmholtz Centre for Environmental Research - UFZ); Lily-belle Sweet (Helmholtz Centre for Environmental Research - UFZ); Gustau Camps-Valls (Universitat de València); Jakob Zscheischler (Helmholtz Centre for Environmental Research - UFZ)

Generative Modeling Climate Science & Modeling Earth Observation & Monitoring Ecosystems & Biodiversity Extreme Weather Forests Interpretable ML Time-series Analysis
NeurIPS 2022 TCFD-NLP: Assessing alignment of climate disclosures using NLP for the financial markets (Papers Track)
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Abstract: Climate-related disclosure is increasing in importance as companies and stakeholders alike aim to reduce their environmental impact and exposure to climate-induced risk. Companies primarily disclose this information in annual or other lengthy documents where climate information is not the sole focus. To assess the quality of a company's climate-related disclosure, these documents, often hundreds of pages long, must be reviewed manually by climate experts. We propose a more efficient approach to assessing climate-related financial information. We construct a model leveraging TF-IDF, sentence transformers and multi-label k nearest neighbors (kNN). The developed model is capable of assessing alignment of climate disclosures at scale, with a level of granularity and transparency that will support decision-making in the financial markets with relevant climate information. In this paper, we discuss the data that enabled this project, the methodology, and how the resulting model can drive climate impact.

Authors: Rylen Sampson (Manifest Climate); Aysha Cotterill (Manifest Climate); Quoc Tien Au (Manifest Climate)

Climate Finance & Economics Natural Language Processing
NeurIPS 2022 Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes (Papers Track)
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Abstract: With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses. Synthetic Aperture Radar (SAR) is a remote sensing technique that can provide measurements of affected areas independent from weather or lighting conditions. Usage of SAR, however, is hindered by domain knowledge that is necessary for the pre-processing steps and its interpretation requires expert knowledge. We provide simplified, pre-processed, machine-learning ready SAR datacubes for four globally located landslide events obtained from several Sentinel-1 satellite passes before and after a landslide triggering event together with segmentation maps of the landslides. From this dataset, using the Hokkaido, Japan datacube, we study the feasibility of SAR-based landslide detection with supervised deep learning (DL). Our results demonstrate that DL models can be used to detect landslides from SAR data, achieving an Area under the Precision-Recall curve exceeding 0.7. We find that additional satellite visits enhance detection performance, but that early detection is possible when SAR data is combined with terrain information from a digital elevation model. This can be especially useful for time-critical emergency interventions.

Authors: Vanessa Boehm (UC Berkeley); Wei Ji Leong (The Ohio State University); Ragini Bal Mahesh (German Aerospace Center DLR); Ioannis Prapas (National Observatory of Athens); Siddha Ganju (Nvidia); Freddie Kalaitzis (University of Oxford); Edoardo Nemni (United Nations Satellite Centre (UNOSAT)); Raul Ramos-Pollan (Universidad de Antioquia)

Disaster Management and Relief Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2022 Hybrid Recurrent Neural Network for Drought Monitoring (Papers Track)
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Abstract: Droughts are pervasive hydrometeorological phenomena and global hazards, whose frequency and intensity are expected to increase in the context of climate change. Drought monitoring is of paramount relevance. Here we propose a hybrid model for drought detection that integrates both climatic indices and data-driven models in a hybrid deep learning approach. We exploit time-series of multi-scale Standardized Precipitation Evapotranspiration Index together with precipitation and temperature as inputs. We introduce a dual-branch recurrent neural network with convolutional lateral connections for blending the data. Experimental and ablative results show that the proposed system outperforms both the considered drought index and purely data-driven deep learning models. Our results suggest the potential of hybrid models for drought monitoring and open the door to synergistic systems that learn from data and domain knowledge altogether.

Authors: Mengxue Zhang (Universitat de València); Miguel-Ángel Fernández-Torres (Universitat de València); Gustau Camps-Valls (Universitat de València)

Climate Science & Modeling Extreme Weather Computer Vision & Remote Sensing Hybrid Physical Models Time-series Analysis
NeurIPS 2022 Deep Learning for Global Wildfire Forecasting (Papers Track)
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Abstract: Climate change is expected to aggravate wildfire activity through the exacerbation of fire weather. Improving our capabilities to anticipate wildfires on a global scale is of uttermost importance for mitigating their negative effects. In this work, we create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale with the use of segmentation deep learning models. Particularly, we present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers (climate, vegetation, oceanic indices, human-related variables), as well as the historical burned areas and wildfire emissions for 2001-2021. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time. Our work motivates the use of deep learning for global burned area forecasting and paves the way towards improved anticipation of global wildfire patterns.

Authors: Ioannis Prapas (National Observatory of Athens); Akanksha Ahuja (NOA); Spyros Kondylatos (National Observatory of Athens); Ilektra Karasante (National Observatory of Athens); Lazaro Alonso (Max Planck Institute for Biogeochemistry); Eleanna Panagiotou (Harokopio University of Athens); Charalampos Davalas (Harokopio University of Athens); Dimitrios Michail (Harokopio University of Athens); Nuno Carvalhais (Max Planck Institute for Biogeochemistry); Ioannis Papoutsis (National Observatory of Athens)

Earth Observation & Monitoring Climate Science & Modeling Disaster Management and Relief Extreme Weather Computer Vision & Remote Sensing
NeurIPS 2022 Causal Modeling of Soil Processes for Improved Generalization (Papers Track)
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Abstract: Measuring and monitoring soil organic carbon is critical for agricultural productivity and for addressing critical environmental problems. Soil organic carbon not only enriches nutrition in soil, but also has a gamut of co-benefits such as improving water storage and limiting physical erosion. Despite a litany of work in soil organic carbon estimation, current approaches do not generalize well across soil conditions and management practices. We empirically show that explicit modeling of cause-and-effect relationships among the soil processes improves the out-of-distribution generalizability of prediction models. We provide a comparative analysis of soil organic carbon estimation models where the skeleton is estimated using causal discovery methods. Our framework provide an average improvement of 81% in test mean squared error and 52% in test mean absolute error.

Authors: Somya Sharma (U. Minnesota); Swati Sharma (Microsoft Research); Emre Kiciman (Microsoft Research); Andy Neal (Rothamstead); Ranveer Chandra (Microsoft Research); John Crawford (University of Glasgow); Sara Malvar (Microsoft); Eduardo R Rodrigues (MSR)

Agriculture & Food Causal & Bayesian Methods
NeurIPS 2022 Machine Learning for Activity-Based Road Transportation Emissions Estimation (Papers Track)
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Abstract: Measuring and attributing greenhouse gas (GHG) emissions remains a challenging problem as the world strives towards meeting emissions reductions targets. As a significant portion of total global emissions, the road transportation sector represents an enormous challenge for estimating and tracking emissions at a global scale. To meet this challenge, we have developed a hybrid approach for estimating road transportation emissions that combines the strengths of machine learning and satellite imagery with localized emissions factors data to create an accurate, globally scalable, and easily configurable GHG monitoring framework.

Authors: Derek Rollend (JHU); Kevin Foster (JHU); Tomek Kott (JHU); Rohita Mocharla (JHU); Rodrigo Rene Rai Munoz Abujder (Johns Hopkins Applied Physics Laboratory); Neil Fendley (JHU/APL); Chace Ashcraft (JHU/APL); Frank Willard (JHU); Marisa Hughes (JHU)

Transportation Cities & Urban Planning Computer Vision & Remote Sensing
NeurIPS 2022 Bayesian State-Space SCM for Deforestation Baseline Estimation for Forest Carbon Credit (Papers Track) Best Paper: Pathway to Impact
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Abstract: In forest carbon credit, the concept of dynamic (or ex-post) baseline has been discussed to overcome the criticism of junk carbon credit, while an ex-ante baseline is still necessary in terms of project finance and risk assessment. We propose a Bayesian state-space SCM, which integrates both ex-ante and ex-post baseline estimation in a time-series causal inference framework. We apply the proposed model to a REDD+ project in Brazil, and show that it might have had a small, positive effect but had been over-credited and that the 90% predictive interval of the ex-ante baseline included the ex-post baseline, implying our ex-ante estimation can work effectively.

Authors: Keisuke Takahata (sustainacraft, Inc.); Hiroshi Suetsugu (sustainacraft, Inc.); Keiichi Fukaya (National Institute for Environmental Studies); Shinichiro Shirota (Hitotsubashi University)

Forests Causal & Bayesian Methods Land Use
NeurIPS 2022 Estimating Corporate Scope 1 Emissions Using Tree-Based Machine Learning Methods (Papers Track)
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Abstract: Companies worldwide contribute to climate change, emitting significant amounts of greenhouse gases (GHGs). Yet, most do not report their direct or Scope 1 emissions, resulting in a large data gap in corporate emissions. This study aims to fill this gap by training several decision-tree machine learning models to predict company-level Scope 1 emissions. Our results demonstrate that the Extreme Gradient Boosting and LightGBM models perform best, where the former shows a 19% improvement in prediction error over a benchmark model. Our model is also of reduced complexity and greater computational efficiency; it does not require meta-learners and is trained on a smaller number of features, for which data is more common and accessible compared to prior works. Our features are uniquely chosen based on concepts of environmental pollution in economic theory. Predicting corporate emissions with machine learning can be used as a gap-filling approach, which would allow for better GHG accounting and tracking, thus facilitating corporate decarbonization efforts in the long term. It can also impact representations of a company’s carbon performance and carbon risks, thereby helping to funnel investments towards companies with lower emissions and those making true efforts to decarbonize.

Authors: Elham Kheradmand (University of Montreal); Maida Hadziosmanovic (Concordia University); Nazim Benguettat (Concordia); H. Damon Matthews (Concordia University); Shannon M. Lloyd (Concordia University)

Climate Finance & Economics Interpretable ML
NeurIPS 2022 Analyzing Micro-Level Rebound Effects of Energy Efficient Technologies (Papers Track)
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Abstract: Energy preservation is central to prevent resource depletion, climate change and environment degradation. Investment in raising efficiency of appliances is among the most significant attempts to save energy. Ironically, introduction of many such energy saving appliances increased the total energy consumption instead of reducing it. This effect in literature is attributed to the inherent Jevons paradox (JP) and optimism bias (OB) in consumer behavior. However, the magnitude of these instincts vary among different people. Identification of this magnitude for each household can enable the development of appropriate policies that induce desired energy saving behaviour. Using the RECS 2015 dataset, the paper uses machine learning for each electrical appliance to determine the dependence of their total energy consumption on their energy star rating. This shows that only substitutable appliances register increase in energy demand upon boosted efficiency. Lastly, an index is noted to indicate the varying influence of JP and OB on different households.

Authors: Mayank Jain (University College Dublin); Mukta Jain (Delhi School of Economics); Tarek T. Alskaif (Wageningen University); Soumyabrata Dev (University College Dublin)

Behavioral and Social Science Cities & Urban Planning Power & Energy
NeurIPS 2022 Comparing the carbon costs and benefits of low-resource solar nowcasting (Papers Track)
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Abstract: Mitigating emissions in line with climate goals requires the rapid integration of low carbon energy sources, such as solar photovoltaics (PV) into the electricity grid. However, the energy produced from solar PV fluctuates due to clouds obscuring the sun's energy. Solar PV yield nowcasting is used to help anticipate peaks and troughs in demand to support grid integration. This paper compares multiple low-resource approaches to nowcasting solar PV yield. To do so, we use a dataset of UK satellite imagery and solar PV energy readings over a 1 to 4-hour time range. Our work investigates the performance of multiple nowcasting models. The paper also estimates the carbon emissions generated and averted by deploying models such as these, and finds that short-term PV forecasting may have a benefit several orders of magnitude greater than its carbon cost and that this benefit holds for small models that could be deployable in low-resource settings around the globe.

Authors: Ben Dixon (UCL); Jacob Bieker (Open Climate Fix); Maria Perez-Ortiz (University College London)

Computer Vision & Remote Sensing Climate Science & Modeling Time-series Analysis
NeurIPS 2022 Climate Policy Tracker: Pipeline for automated analysis of public climate policies (Papers Track)
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Abstract: The number of standardized policy documents regarding climate policy and their publication frequency is significantly increasing. The documents are long and tedious for manual analysis, especially for policy experts, lawmakers, and citizens who lack access or domain expertise to utilize data analytics tools. Potential consequences of such a situation include reduced citizen governance and involvement in climate policies and an overall surge in analytics costs, rendering less accessibility for the public. In this work, we use a Latent Dirichlet Allocation-based pipeline for the automatic summarization and analysis of 10-years of national energy and climate plans (NECPs) for the period from 2021 to 2030, established by 27 Member States of the European Union. We focus on analyzing policy framing, the language used to describe specific issues, to detect essential nuances in the way governments frame their climate policies and achieve climate goals. The methods leverage topic modeling and clustering for the comparative analysis of policy documents across different countries. It allows for easier integration in potential user-friendly applications for the development of theories and processes of climate policy. This would further lead to better citizen governance and engagement over climate policies and public policy research.

Authors: Artur Żółkowski (Warsaw University of Technology); Mateusz Krzyziński (Warsaw University of Technology); Piotr Wilczyński (Warsaw University of Technology); Stanisław Giziński (University of Warsaw); Emilia Wiśnios (University of Warsaw); Bartosz Pieliński (University of Warsaw); Julian Sienkiewicz (Warsaw University of Technology); Przemysław Biecek (Warsaw University of Technology)

Public Policy Natural Language Processing
NeurIPS 2022 Topic correlation networks inferred from open-ended survey responses reveal signatures of ideology behind carbon tax opinion (Papers Track)
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Abstract: Ideology can often render policy design ineffective by overriding what, at face value, are rational incentives. A timely example is carbon pricing, whose public support is strongly influenced by ideology. As a system of ideas, ideology expresses itself in the way people explain themselves and the world. As an object of study, ideology is then amenable to a generative modelling approach within the text-as-data paradigm. Here, we analyze the structure of ideology underlying carbon tax opinion using topic models. An idea, termed a topic, is operationalized as the fixed set of proportions with which words are used when talking about it. We characterize ideology through the relational structure between topics. To access this latent structure, we use the highly expressive Structural Topic Model to infer topics and the weights with which individual opinions mix topics. We fit the model to a large dataset of open-ended survey responses of Canadians elaborating on their support of or opposition to the tax. We propose and evaluate statistical measures of ideology in our data, such as dimensionality and heterogeneity. Finally, we discuss the implications of the results for transition policy in particular, and of our approach to analyzing ideology for computational social science in general.

Authors: Maximilian Puelma Touzel (Mila)

Behavioral and Social Science Climate Finance & Economics Public Policy Generative Modeling Natural Language Processing
NeurIPS 2022 Controllable Generation for Climate Modeling (Papers Track)
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Abstract: Recent years have seen increased interest in modeling future climate trends, especially from the point of view of accurately predicting, understanding and mitigating downstream impacts. For instance, current state-of-the-art process-based agriculture models rely on high-resolution climate data during the growing season for accurate estimation of crop yields. However, high-resolution climate data for future climates is unavailable and needs to be simulated, and that too for multiple possible climate scenarios, which becomes prohibitively expensive via traditional methods. Meanwhile, deep generative models leveraging the expressivity of neural networks have shown immense promise in modeling distributions in high dimensions. Here, we cast the problem of simulation of climate scenarios in a generative modeling framework. Specifically, we leverage the GAN (Generative Adversarial Network) framework for simulating synthetic climate scenarios. We condition the model by quantifying the degree of ``extremeness" of the observed sample, which allows us to sample from different parts of the distribution. We demonstrate the efficacy of the proposed method on the CHIRPS precipitation dataset.

Authors: Moulik Choraria (University of Illinois at Urbana-Champaign); Daniela Szwarcman (IBM Research); Bianca Zadrozny (IBM Research); Campbell D Watson (IBM Reserch); Lav Varshney (UIUC: ECE)

Climate Science & Modeling Extreme Weather Generative Modeling
NeurIPS 2022 Learn to Bid: Deep Reinforcement Learning with Transformer for Energy Storage Bidding in Energy and Contingency Reserve Markets (Papers Track)
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Abstract: As part of efforts to tackle climate change, grid-scale battery energy storage systems (BESS) play an essential role in facilitating reliable and secure power system operation with variable renewable energy (VRE). BESS can balance time-varying electricity demand and supply in the spot market through energy arbitrage and in the frequency control ancillary services (FCAS) market through service enablement or delivery. Effective algorithms are needed for the optimal participation of BESS in multiple markets. Using deep reinforcement learning (DRL), we present a BESS bidding strategy in the joint spot and contingency FCAS markets, leveraging a transformer-based temporal feature extractor to exploit the temporal trends of volatile energy prices. We validate our strategy on real-world historical energy prices in the Australian National Electricity Market (NEM). We demonstrate that the novel DRL-based bidding strategy significantly outperforms benchmarks. The simulation also reveals that the joint bidding in both the spot and contingency FCAS markets can yield a much higher profit than in individual markets. Our work provides a viable use case for the BESS, contributing to the power system operation with high penetration of renewables.

Authors: Jinhao Li (Monash University); Changlong Wang (Monash University); Yanru Zhang (University of Electronic Science and Technology of China); Hao Wang (Monash University)

Power & Energy Reinforcement Learning
NeurIPS 2022 Curriculum Based Reinforcement Learning to Avert Cascading Failures in the Electric Grid (Papers Track)
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Abstract: We present an approach to integrate the domain knowledge of the electric power grid operations into reinforcement learning (RL) frameworks for effectively learning RL agents to prevent cascading failures. A curriculum-based approach with reward tuning is incorporated into the training procedure by modifying the environment using the network physics. Our procedure is tested on an actor-critic-based agent on the IEEE 14-bus test system using the RL environment developed by RTE, the French transmission system operator (TSO). We observed that naively training the RL agent without the curriculum approach failed to prevent cascading for most test scenarios, while the curriculum based RL agents succeeded in most test scenarios, illustrating the importance of properly integrating domain knowledge of physical systems for real-world RL applications.

Authors: Amarsagar Reddy Ramapuram Matavalam (Arizona State University); Kishan Guddanti (Pacific Northwest National Lab); Yang Weng (Arizona State University)

Power & Energy Reinforcement Learning
NeurIPS 2022 Short-range forecasts of global precipitation using deep learning-augmented numerical weather prediction (Papers Track)
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Abstract: Precipitation drives the hydroclimate of Earth and its spatiotemporal changes on a day to day basis have one of the most notable socioeconomic impacts. The success of numerical weather prediction (NWP) is measured by the improvement of forecasts for various physical fields such as temperature and pressure. Large biases however exist in the precipitation predictions. Pure deep learning based approaches lack the advancements acheived by NWP in the past two to three decades. Hybrid methodology using NWP outputs as inputs to the deep learning based refinement tool offer an attractive means taking advantage of both NWP and state of the art deep learning algorithms. Augmenting the output from a well-known NWP model: Coupled Forecast System ver.2 (CFSv2) with deep learning for the first time, we demonstrate a hybrid model capability (DeepNWP) which shows substantial skill improvements for short-range global precipitation at 1-, 2- and 3-days lead time. To achieve this hybridization, we address the sphericity of the global data by using modified DLWP-CS architecture which transforms all the fields to cubed-sphere projection. The dynamical model outputs corresponding to precipitation and surface temperature are ingested to a UNET for predicting the target ground truth precipitation. While the dynamical model CFSv2 shows a bias in the range of +5 to +7 mm/day over land, the multivariate deep learning model reduces it to -1 to +1 mm/day over global land areas. We validate the results by taking examples from Hurricane Katrina in 2005, Hurricane Ivan in 2004, Central European floods in 2010, China floods in 2010, India floods in 2005 and the Myanmar cyclone Nargis in 2008.

Authors: Manmeet Singh (The University of Texas at Austin); Vaisakh SB (Indian Institute of Tropical Meteorology); Nachiketa Acharya (Department of Meteorology and Atmospheric Science,Pennsylvania State University); Aditya Grover (UCLA); Suryachandra A. Rao (Indian Institute of Tropical Meteorology); Bipin Kumar (Indian Institute of Tropical Meteorology); Zong-Liang Yang (The University of Texas at Austin); Dev Niyogi (The University of Texas at Austin)

Climate Science & Modeling Hybrid Physical Models
NeurIPS 2022 A Multi-Scale Deep Learning Framework for Projecting Weather Extremes (Papers Track) Best Paper: ML Innovation
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Abstract: Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year. Under climate change, their impact and intensity are expected to worsen significantly. Unfortunately, general circulation models (GCMs), which are currently the primary tool for climate projections, cannot characterize weather extremes accurately. To address this, we present a multi-resolution deep-learning framework that, firstly, corrects a GCM's biases by matching low-order and tail statistics of its output with observations at coarse scales; and secondly, increases the level of detail of the debiased GCM output by reconstructing the finer scales as a function of the coarse scales. We use the proposed framework to generate statistically realistic realizations of the climate over Western Europe from a simple GCM corrected using observational atmospheric reanalysis. We also discuss implications for probabilistic risk assessment of natural disasters in a changing climate.

Authors: Antoine Blanchard (MIT); Nishant Parashar (Verisk Analytics); Boyko Dodov (Verisk Analytics); Christian Lessig (Otto-von-Guericke-Universitat Magdeburg); Themis Sapsis (MIT)

Extreme Weather Hybrid Physical Models
NeurIPS 2022 A Global Classification Model for Cities using ML (Papers Track)
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Abstract: This paper develops a novel data set for three key resources use; namely, food, water, and energy, for 9000 cities globally. The data set is then utilized to develop a clustering approach as a starting point towards a global classification model. This novel clustering approach aims to contribute to developing an inclusive view of resource efficiency for all urban centers globally. The proposed clustering algorithm is comprised of three steps: first, outlier detection to address specific city characteristics, then a Variational Autoencoder (VAE), and finally, Agglomerative Clustering (AC) to improve the classification results. Our results show that this approach is more robust and yields better results in creating delimited clusters with high Calinski-Harabasz Index scores and Silhouette Coefficient than other baseline clustering methods.

Authors: Doron Hazan (MIT); Mohamed Habashy (Massachusetts Institute of Technology); Mohanned ElKholy (Massachusetts Institute of Technology); Omer Mousa (American University in Cairo); Norhan M Bayomi (MIT Environmental Solutions Initiative); Matias Williams (Massachusetts Institute of Technology); John Fernandez (Massachusetts Institute of Technology)

Cities & Urban Planning Data Mining
NeurIPS 2022 EnhancedSD: Downscaling Solar Irradiance from Climate Model Projections (Papers Track)
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Abstract: Renewable energy-based electricity systems are seen as a keystone of future decarbonization efforts. However, power system planning does not currently consider the impacts of climate change on renewable energy resources such as solar energy, chiefly due to a paucity of climate-impacted high-resolution solar power data. Existing statistical downscaling (SD) methods that learn to map coarse-resolution versions of historical reanalysis data to generate finer resolution outputs are of limited use when applied to future climate model projections due to the domain gap between climate models and reanalysis data. In contrast, we present EnhancedSD, a deep learning-based framework for downscaling coarse-scale climate model outputs to high-resolution observational (reanalysis) data. Our proposed ML based downscaling allows for future reanalysis projections, which can be pivotal for mitigating climate change’s impacts on power systems planning.

Authors: Nidhin Harilal (University of Colorado, Boulder); Bri-Mathias S Hodge (University of Colorado Boulder); Claire Monteleoni (University of Colorado Boulder); Aneesh Subramanian (University of California, San Diego)

Computer Vision & Remote Sensing Climate Science & Modeling Power & Energy
NeurIPS 2022 Positional Encoder Graph Neural Networks for Geographic Data (Papers Track)
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Abstract: Modeling spatial dependencies in geographic data is of crucial importance for the modeling of our planet. Graph neural networks (GNNs) provide a powerful and scalable solution for modeling continuous spatial data. However, in the absence of further context on the geometric structure of the data, they often rely on Euclidean distances to construct the input graphs. This assumption can be improbable in many real-world settings, where the spatial structure is more complex and explicitly non-Euclidean (e.g., road networks). In this paper, we propose PE-GNN, a new framework that incorporates spatial context and correlation explicitly into the models. Building on recent advances in geospatial auxiliary task learning and semantic spatial embeddings, our proposed method (1) learns a context-aware vector encoding of the geographic coordinates and (2) predicts spatial autocorrelation in the data in parallel with the main task. We show the effectiveness of our approach on two climate-relevant regression tasks: 3d spatial interpolation and air temperature prediction. The code for this study can be accessed via: https://bit.ly/3xDpfyV.

Authors: Konstantin Klemmer (Microsoft Research); Nathan S Safir (University of Georgia); Daniel B Neill (New York University)

Climate Science & Modeling Earth Observation & Monitoring Extreme Weather
NeurIPS 2022 Image-based Early Detection System for Wildfires (Papers Track)
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Abstract: Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life. Due to the warmer and drier conditions created by climate change, more severe and uncontrollable wildfires are expected to occur in the coming years. This could lead to a global wildfire crisis and have dire consequences on our planet. Hence, it has become imperative to use technology to help prevent the spread of wildfires. One way to prevent the spread of wildfires before they become too large is to perform early detection i.e, detecting the smoke before the actual fire starts. In this paper, we present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy and can send immediate alerts to users. Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily. We show that our system has a high true detection rate and a low false detection rate. Our performance evaluation study also shows that on an average our system detects wildfire smoke faster than an actual person.

Authors: Omkar Ranadive (Alchera X); Jisu Kim (Alchera); Serin Lee (Alchera X); Youngseo Cha (Alchera); Heechan Park (Alchera); Minkook Cho (Alchera); Young Hwang (Alchera)

Computer Vision & Remote Sensing Disaster Management and Relief
NeurIPS 2022 Towards Global Crop Maps with Transfer Learning (Papers Track)
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Abstract: The continuous increase in global population and the impact of climate change on crop production are expected to affect the food sector significantly. In this context, there is need for timely, large-scale and precise mapping of crops for evidence-based decision making. A key enabler towards this direction are new satellite missions that freely offer big remote sensing data of high spatio-temporal resolution and global coverage. During the previous decade and because of this surge of big Earth observations, deep learning methods have dominated the remote sensing and crop mapping literature. Nevertheless, deep learning models require large amounts of annotated data that are scarce and hard-to-acquire. To address this problem, transfer learning methods can be used to exploit available annotations and enable crop mapping for other regions, crop types and years of inspection. In this work, we have developed and trained a deep learning model for paddy rice detection in South Korea using Sentinel-1 VH time-series. We then fine-tune the model for i) paddy rice detection in France and Spain and ii) barley detection in the Netherlands. Additionally, we propose a modification in the pre-trained weights in order to incorporate extra input features (Sentinel-1 VV). Our approach shows excellent performance when transferring in different areas for the same crop type and rather promising results when transferring in a different area and crop type.

Authors: Hyun-Woo Jo (Korea University); Alkiviadis Marios Koukos (National Observatory of Athens); Vasileios Sitokonstantinou (National Observatory of Athens); Woo-Kyun Lee (Korea University); Charalampos Kontoes (National Observatory of Athens)

Meta- and Transfer Learning Agriculture & Food Earth Observation & Monitoring Forests Computer Vision & Remote Sensing
NeurIPS 2022 Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds (Papers Track)
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Abstract: Pyrocumulonimbus (pyroCb) clouds are storm clouds generated by extreme wildfires. PyroCbs are associated with unpredictable, and therefore dangerous, wildfire spread. They can also inject smoke particles and trace gases into the upper troposphere and lower stratosphere, affecting the Earth's climate. As global temperatures increase, these previously rare events are becoming more common. Being able to predict which fires are likely to generate pyroCb is therefore key to climate adaptation in wildfire-prone areas. This paper introduces Pyrocast, a pipeline for pyroCb analysis and forecasting. The pipeline's first two components, a pyroCb database and a pyroCb forecast model, are presented. The database brings together geostationary imagery and environmental data for over 148 pyroCb events across North America, Australia, and Russia between 2018 and 2022. Random Forests, Convolutional Neural Networks (CNNs), and CNNs pretrained with Auto-Encoders were tested to predict the generation of pyroCb for a given fire six hours in advance. The best model predicted pyroCb with an AUC of 0.90±0.04.

Authors: Kenza Tazi (University of Cambridge); Emiliano Díaz Salas-Porras (University of Valencia); Ashwin Braude (Institut Pierre-Simon Laplace); Daniel Okoh (National Space Research and Development Agency); Kara D. Lamb (Columbia University); Duncan Watson-Parris (University of Oxford); Paula Harder (Fraunhofer ITWM); Nis Meinert (Pasteur Labs)

Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2022 Evaluating Digital Tools for Sustainable Agriculture using Causal Inference (Papers Track)
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Abstract: In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can offer high-performing predictive functionalities, they lack tangible quantitative evidence on their benefits to the farmers. Field experiments can derive such evidence, but are often costly and time consuming. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators. This way, we can increase farmers' trust via enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to increase productivity and secure a sustainable and resilient agriculture against a changing climate. As a case study, we perform an empirical evaluation of a recommendation system for optimal cotton sowing, which was used by a farmers' cooperative during the growing season of 2021. We leverage agricultural knowledge to develop the causal graph of the farm system, we use the back-door criterion to identify the impact of recommendations on the yield and subsequently we estimate it using several methods on observational data. The results showed that a field sown according to our recommendations enjoyed a significant increase in yield 12% to 17%.

Authors: Ilias Tsoumas (National Observatory of Athens); Georgios Giannarakis (National Observatory of Athens); Vasileios Sitokonstantinou (National Observatory of Athens); Alkiviadis Marios Koukos (National Observatory of Athens); Dimitra A Loka (Hellenic Agricultural Organization ELGO DIMITRA); Nikolaos S Bartsotas (National Observatory of Athens); Charalampos Kontoes (National Observatory of Athens); Ioannis N Athanasiadis (Wageningen University and Research)

Causal & Bayesian Methods Agriculture & Food Earth Observation & Monitoring Extreme Weather Societal Adaptation & Resilience
NeurIPS 2022 Generating physically-consistent high-resolution climate data with hard-constrained neural networks (Papers Track)
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Abstract: The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often can only make coarse resolution predictions. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often using image super-resolution methods from computer vision. Despite achieving visually compelling results in some cases, such models often violate conservation laws when predicting physical variables. In order to conserve important physical quantities, we developed a deep downscaling method that guarantees physical constraints are satisfied, by adding a renormalization layer at the end of the neural network. Furthermore, the constrained model also improves the performance according to standard metrics. We show the applicability of our methods across different popular architectures and upsampling factors using ERA5 reanalysis data.

Authors: Paula Harder (Mila); Qidong Yang (New York University); Venkatesh Ramesh (Mila); Prasanna Sattigeri (IBM Research); Alex Hernandez-Garcia (Mila - Quebec AI Institute); Campbell D Watson (IBM Reserch); Daniela Szwarcman (IBM Research); David Rolnick (McGill University, Mila)

Climate Science & Modeling
NeurIPS 2022 Transformers for Fast Emulation of Atmospheric Chemistry Box Models (Papers Track)
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Abstract: When modeling atmospheric chemistry, concentrations are determined by numerically solving large systems of ordinary differential equations that represent a set of chemical reactions. These solvers can be very computationally intensive, particularly those with the thousands or tens of thousands of chemical species and reactions that make up the most accurate models. We demonstrate the application of a deep learning transformer architecture to emulate an atmospheric chemistry box model, and show that this attention-based model outperforms LSTM and autoencoder baselines while providing interpretable predictions that are more than 2 orders of magnitude faster than a numerical solver. This work is part of a larger study to replace the numerical solver in a 3D global chemical model with a machine learned emulator and achieve significant speedups for global climate simulations.

Authors: Herbie Bradley (University of Cambridge); Nathan Luke Abraham (National Centre for Atmospheric Science, UK); Peer Nowack (Imperial College London); Doug McNeall (Met Office Hadley Centre, UK)

Climate Science & Modeling Hybrid Physical Models Time-series Analysis
NeurIPS 2022 Flood Prediction with Graph Neural Networks (Papers Track)
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Abstract: Climate change is increasing the frequency of flooding around the world. As a consequence, there is a growing demand for effective flood prediction. Machine learning is a promising alternative to hydrodynamic models for flood prediction. However, existing approaches focus on capturing either the spatial or temporal flood patterns using CNNs or RNNs, respectively. In this work, we propose FloodGNN, which is a graph neural network (GNN) for flood prediction. Compared to existing approaches, FloodGNN (i) employs a graph-based model (GNN); (ii) operates on both spatial and temporal dimensions; and (iii) processes the water flow velocities as vector features, instead of scalar features. Experiments show that FloodGNN achieves promising results, outperforming an RNN-based baseline.

Authors: Arnold N Kazadi (Rice University); James Doss-Gollin (Rice University); Antonia Sebastian (UNC Chapel Hill); Arlei Silva (Rice University)

Extreme Weather
NeurIPS 2022 Neural Representation of the Stratospheric Ozone Layer (Papers Track)
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Abstract: In climate modeling, the stratospheric ozone layer is typically only considered in a highly simplified form due to computational constraints. For climate projections, it would be of advantage to include the mutual interactions between stratospheric ozone, temperature, and atmospheric dynamics to accurately represent radiative forcing. The overarching goal of our research is to replace the ozone chemistry in climate models with a machine-learned neural representation of the stratospheric ozone chemistry that allows for a particularly fast, but accurate and stable simulation. We created a benchmark data set from pairs of input and output variables that we stored from simulations of a chemistry and transport model. We analyzed several variants of multilayer perceptrons suitable for physical problems to learn a neural representation of a function that predicts 24-hour ozone tendencies based on input variables. We performed a comprehensive hyperparameter optimization of the multilayer perceptron using Bayesian search and Hyperband early stopping. We validated our model by implementing it in a chemistry and transport model and comparing computation time, accuracy, and stability with the full chemistry module. We found that our model had a computation time that was a factor of 700 faster than the full chemistry module. The accuracy of our model compares favorably to the full chemistry module within a two-year simulation run, also outperforms a previous polynomial approach for fast ozone chemistry, and reproduces seasonality well in both hemispheres. In conclusion, the neural representation of stratospheric ozone chemistry in simulation resulted in an ozone layer that showed a high accuracy, significant speed-up, and stability in a long-term simulation.

Authors: Helge Mohn (Alfred Wegener Institute for Polar and Marine Research); Daniel Kreyling (Alfred Wegener Institute for Polar and Marine Research); Ingo Wohltmann (Alfred Wegener Institute for Polar and Marine Research); Ralph Lehmann (Alfred Wegener Institute for Polar and Marine Research); Peter Maaß (University of Bremen); Markus Rex (Alfred Wegener Institute for Polar and Marine Research)

Climate Science & Modeling
NeurIPS 2022 DL-Corrector-Remapper: A grid-free bias-correction deep learning methodology for data-driven high-resolution global weather forecasting (Papers Track)
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Abstract: Data-driven models, such as FourCastNet (FCN), have shown exemplary performance in high-resolution global weather forecasting. This performance, however, is based on supervision on mesh-gridded weather data without the utilization of raw climate observational data, the gold standard ground truth. In this work we develop a methodology to correct, remap, and fine-tune gridded uniform forecasts of FCN so it can be directly compared against observational ground truth, which is sparse and non-uniform in space and time. This is akin to bias-correction and post-processing of numerical weather prediction (NWP), a routine operation at meteorological and weather forecasting centers across the globe. The Adaptive Fourier Neural Operator (AFNO) architecture is used as the backbone to learn continuous representations of the atmosphere. The spatially and temporally non-uniform output is evaluated by the non-uniform discrete inverse Fourier transform (NUIDFT) given the output query locations. We call this network the Deep-Learning-Corrector-Remapper (DLCR). The improvement in DLCR’s performance against the gold standard ground truth over the baseline’s performance shows its potential to correct, remap, and fine-tune the mesh-gridded forecasts under the supervision of observations.

Authors: Tao Ge (Washington University in St. Louis); Jaideep Pathak (NVIDIA Corporation); Akshay Subramaniam (NVIDIA); Karthik Kashinath (NVIDIA)

Climate Science & Modeling Extreme Weather
NeurIPS 2022 Industry-scale CO2 Flow Simulations with Model-Parallel Fourier Neural Operators (Papers Track)
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Abstract: Carbon capture and storage (CCS) is one of the most promising technologies for reducing greenhouse gas emissions and relies on numerical reservoir simulations for identifying and monitoring CO2 storage sites. In many commercial settings however, numerical reservoir simulations are too computationally expensive for important downstream application such as optimization or uncertainty quantification. Deep learning-based surrogate models offer the possibility to solve PDEs many orders of magnitudes faster than conventional simulators, but they are difficult to scale to industrial-scale problem settings. Using model-parallel deep learning, we train the largest CO2 surrogate model to date on a 3D simulation grid with two million grid points. To train the 3D simulator, we generate a new training dataset based on a real-world CCS simulation benchmark. Once trained, each simulation with the network is five orders of magnitude faster than a numerical reservoir simulator and 4,500 times cheaper. This paves the way to applications that require thousands of (sequential) simulations, such as optimizing the location of CO2 injection wells to maximize storage capacity and minimize risk of leakage.

Authors: Philipp A Witte (Microsoft); Russell Hewett (Microsoft); Ranveer Chandra (Microsoft Research)

Carbon Capture & Sequestration
NeurIPS 2022 Adaptive Bias Correction for Improved Subseasonal Forecasting (Papers Track)
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Abstract: Subseasonal forecasting — predicting temperature and precipitation 2 to 6 weeks ahead — is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remains poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. To counter these errors, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. When applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60-90% and precipitation forecasting skill by 40-69% in the contiguous U.S. We couple these performance improvements with a practical workflow, based on Cohort Shapley, for explaining ABC skill gains and identifying higher-skill windows of opportunity based on specific climate conditions.

Authors: Soukayna Mouatadid (University of Toronto); Paulo Orenstein (IMPA); Genevieve E Flaspohler (MIT); Judah Cohen (AER); Miruna Oprescu (Cornell University); Ernest Fraenkel (MIT); Lester Mackey (Microsoft Research New England)

Climate Science & Modeling Extreme Weather
NeurIPS 2022 Nowformer : A Locally Enhanced Temporal Learner for Precipitation Nowcasting (Papers Track)
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Abstract: The precipitation video datasets have distinctive meteorological patterns where a mass of fluid moves in a particular direction across the entire frames, and each local area of the fluid has an individual life cycle from initiation to maturation to decay. This paper proposes a novel transformer-based model for precipitation nowcasting that can extract global and local dynamics within meteorological characteristics. The experimental results show our model achieves state-of-the-art performances on the precipitation nowcasting benchmark.

Authors: Jinyoung Park (KAIST); Inyoung Lee (KAIST); Minseok Son (KAIST); Seungju Cho (KAIST); Changick Kim (KAIST)

Computer Vision & Remote Sensing Earth Observation & Monitoring
NeurIPS 2022 An Unsupervised Learning Perspective on the Dynamic Contribution to Extreme Precipitation Changes (Papers Track)
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Abstract: Despite the importance of quantifying how the spatial patterns of extreme precipitation will change with warming, we lack tools to objectively analyze the storm-scale outputs of modern climate models. To address this gap, we develop an unsupervised machine learning framework to quantify how storm dynamics affect precipitation extremes and their changes without sacrificing spatial information. Over a wide range of precipitation quantiles, we find that the spatial patterns of extreme precipitation changes are dominated by spatial shifts in storm regimes rather than intrinsic changes in how these storm regimes produce precipitation.

Authors: Griffin S Mooers (UC Irvine); Tom Beucler (University of Lausanne); Michael Pritchard (UCI); Stephan Mandt (University of California, Irivine)

Climate Science & Modeling Unsupervised & Semi-Supervised Learning
NeurIPS 2022 An Interpretable Model of Climate Change Using Correlative Learning (Papers Track)
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Abstract: Determining changes in global temperature and precipitation that may indicate climate change is complicated by annual variations. One approach for finding potential climate change indicators is to train a model that predicts the year from annual means of global temperatures and precipitations. Such data is available from the CMIP6 ensemble of simulations. Here a two-hidden-layer neural network trained on this data successfully predicts the year. Differences among temperature and precipitation patterns for which the model predicts specific years reveal changes through time. To find these optimal patterns, a new way of interpreting what the neural network has learned is explored. Alopex, a stochastic correlative learning algorithm, is used to find optimal temperature and precipitation maps that best predict a given year. These maps are compared over multiple years to show how temperature and precipitations patterns indicative of each year change over time.

Authors: Charles Anderson (Colorado State University); Jason Stock (Colorado State University)

Climate Science & Modeling Interpretable ML
NeurIPS 2022 Multimodal Wildland Fire Smoke Detection (Papers Track)
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Abstract: Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have in turn led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgency to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. In this paper, we present our work on integrating multiple data sources in SmokeyNet, a deep learning model using spatio-temporal information to detect smoke from wildland fires. Camera image data is integrated with weather sensor measurements and processed by SmokeyNet to create a multimodal wildland fire smoke detection system. Our results show that incorporating multimodal data in SmokeyNet improves performance in terms of both F1 and time-to-detection over the baseline with a single data source. With a time-to-detection of only a few minutes, SmokeyNet can serve as an automated early notification system, providing a useful tool in the fight against destructive wildfires.

Authors: Mai Nguyen (University of California San Diego); Shreyas Anantha Ramaprasad (University of California San Diego); Jaspreet Kaur Bhamra (University of California San Diego); Siddhant Baldota (University of California San Diego); Garrison Cottrell (UC San Diego)

Disaster Management and Relief Computer Vision & Remote Sensing
NeurIPS 2022 Using uncertainty-aware machine learning models to study aerosol-cloud interactions (Papers Track)
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Abstract: Aerosol-cloud interactions (ACI) include various effects that result from aerosols entering a cloud, and affecting cloud properties. In general, an increase in aerosol concentration results in smaller droplet sizes which leads to larger, brighter, longer-lasting clouds that reflect more sunlight and cool the Earth. The strength of the effect is however heterogeneous, meaning it depends on the surrounding environment, making ACI one of the most uncertain effects in our current climate models. In our work, we use causal machine learning to estimate ACI from satellite observations by reframing the problem as a treatment (aerosol) and outcome (change in droplet radius). We predict the causal effect of aerosol on clouds with uncertainty bounds depending on the unknown factors that may be influencing the impact of aerosol. Of the three climate models evaluated, we find that only one plausibly recreates the trend, lending more credence to its estimate cooling due to ACI.

Authors: Maëlys Solal (University of Oxford); Andrew Jesson (University of Oxford); Yarin Gal (University of Oxford); Alyson Douglas (University of Oxford)

Causal & Bayesian Methods Climate Science & Modeling Earth Observation & Monitoring Uncertainty Quantification & Robustness
NeurIPS 2022 Accessible Large-Scale Plant Pathology Recognition (Papers Track)
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Abstract: Plant diseases are costly and threaten agricultural production and food security worldwide. Climate change is increasing the frequency and severity of plant diseases and pests. Therefore, detection and early remediation can have a significant impact, especially in developing countries. However, AI solutions are yet far from being in production. The current process for plant disease diagnostic consists of manual identification and scoring by humans, which is time-consuming, low-supply, and expensive. Although computer vision models have shown promise for efficient and automated plant disease identification, there are limitations for real-world applications: a notable variation in visual symptoms of a single disease, different light and weather conditions, and the complexity of the models. In this work, we study the performance of efficient classification models and training "tricks" to solve this problem. Our analysis represents a plausible solution for these ecological disasters and might help to assist producers worldwide. More information available at: https://github.com/mv-lab/mlplants

Authors: Marcos V. Conde (University of Würzburg); Dmitry Gordeev (H2O.ai)

Computer Vision & Remote Sensing Disaster Management and Relief Forests Health Active Learning
NeurIPS 2022 Dynamic weights enabled Physics-Informed Neural Network for simulating the mobility of Engineered Nano Particles in a contaminated aquifer (Papers Track)
Abstract and authors: (click to expand)

Abstract: Numerous polluted groundwater sites across the globe require an active remediation strategy for the restoration of natural environmental conditions and local ecosystem. The Engineered Nanoparticles (ENPs) has emerged as an efficient reactive agent for the in-situ degradation of groundwater contaminants. While the performance of these ENPs has been highly promising on the laboratory scale, their application in a real field case conditions is still limited. The optimized injection of the ENPs in the contaminated aquifer and its subsequent monitoring are hindered by the complex transport and retention mechanisms of ENPs. Therefore, a predictive tool for understanding the transport and retention behavior of ENPs becomes highly important. The existing tools in the literature are dominated with numerical simulators, which have limited flexibility and accuracy in the presence of sparse dataset. In this work, a dynamic weights enabled Physics-Informed Neural network (dw-PINN) framework is applied to model the nano-particle´s behavior within an aquifer. The result from the forward model demonstrates the effective capability of dw-PINN in accurately predicting the ENPs mobility. The model verification step shows that the mean squared error of the predicted ENPs concentration using dw-PINN converges to a minimum value of 1.3e-5. In the subsequent step, the result from the inverse model estimates the governing parameters of ENPs mobility with reasonable accuracy. The research work demonstrates the tool´s capability in providing predictive insights for the development of an efficient groundwater remediation strategy.

Authors: Shikhar Nilabh (Amphos21)

Climate Science & Modeling Hybrid Physical Models
NeurIPS 2022 Calibration of Large Neural Weather Models (Papers Track)
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Abstract: Uncertainty quantification of weather forecasts is a necessity for reliably planning for and responding to extreme weather events in a warming world. This motivates the need for well-calibrated ensembles in probabilistic weather forecasting. We present initial results for the calibration of large-scale deep neural weather models for data-driven probabilistic weather forecasting. By explicitly accounting for uncertainties about the forecast's initial condition and model parameters, we generate ensemble forecasts that show promising results on standard diagnostics for probabilistic forecasts. Specifically, we are approaching the Integrated Forecasting System (IFS), the gold standard on probabilistic weather forecasting, on: (i) the spread-error agreement; and (ii) the Continuous Ranked Probability Score (CRPS). Our approach scales to state-of-the-art data-driven weather models, enabling cheap post-hoc calibration of pretrained models with tens of millions of parameters and paving the way towards the next generation of well-calibrated data-driven weather models.

Authors: Andre Graubner (Nvidia); Kamyar Kamyar Azizzadenesheli (Nvidia); Jaideep Pathak (NVIDIA Corporation); Morteza Mardani (Nvidia); Mike Pritchard (Nvidia); Karthik Kashinath (Nvidia); Anima Anandkumar (NVIDIA/Caltech)

Uncertainty Quantification & Robustness Climate Science & Modeling
NeurIPS 2022 Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs (Papers Track)
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Abstract: Forecasting the state of vegetation in response to climate and weather events is a major challenge. Its implementation will prove crucial in predicting crop yield, forest damage, or more generally the impact on ecosystems services relevant for socio-economic functioning, which if absent can lead to humanitarian disasters. Vegetation status depends on weather and environmental conditions that modulate complex ecological processes taking place at several timescales. Interactions between vegetation and different environmental drivers express responses at instantaneous but also time-lagged effects, often showing an emerging spatial context at landscape and regional scales. We formulate the land surface forecasting task as a strongly guided video prediction task where the objective is to forecast the vegetation developing at very fine resolution using topography and weather variables to guide the prediction. We use a Convolutional LSTM (ConvLSTM) architecture to address this task and predict changes in the vegetation state in Africa using Sentinel-2 satellite NDVI, having ERA5 weather reanalysis, SMAP satellite measurements, and topography (DEM of SRTMv4.1) as variables to guide the prediction. Ours results highlight how ConvLSTM models can not only forecast the seasonal evolution of NDVI at high resolution, but also the differential impacts of weather anomalies over the baselines. The model is able to predict different vegetation types, even those with very high NDVI variability during target length.

Authors: Claire Robin (Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany); Christian Requena-Mesa (Computer Vision Group, Friedrich Schiller University Jena; DLR Institute of Data Science, Jena; Max Planck Institute for Biogeochemistry, Jena); Vitus Benson (Max-Planck-Institute for Biogeochemistry); Jeran Poehls (Max-Planck-Institute for Biogeochemistry); Lazaro Alonzo (Max-Planck-Institute for Biogeochemistry Max-Planck-Institute for Biogeochemistry); Nuno Carvalhais (Max-Planck-Institute for Biogeochemistry); Markus Reichstein (Max Planck Institute for Biogeochemistry, Jena; Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena)

Computer Vision & Remote Sensing Agriculture & Food Ecosystems & Biodiversity Extreme Weather Time-series Analysis
NeurIPS 2022 Generative Modeling of High-resolution Global Precipitation Forecasts (Papers Track)
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Abstract: Forecasting global precipitation patterns and, in particular, extreme precipitation events is of critical importance to preparing for and adapting to climate change. Making accurate high-resolution precipitation forecasts using traditional physical models remains a major challenge in operational weather forecasting as they incur substantial computational costs and struggle to achieve sufficient forecast skill. Recently, deep-learning-based models have shown great promise in closing the gap with numerical weather prediction (NWP) models in terms of precipitation forecast skill, opening up exciting new avenues for precipitation modeling. However, it is challenging for these deep learning models to fully resolve the fine-scale structures of precipitation phenomena and adequately characterize the extremes of the long-tailed precipitation distribution. In this work, we present several improvements to the architecture and training process of a current state-of-the art deep learning precipitation model (FourCastNet) using a novel generative adversarial network (GAN) to better capture fine scales and extremes. Our improvements achieve superior performance in capturing the extreme percentiles of global precipitation, while comparable to state-of-the-art NWP models in terms of forecast skill at 1--2 day lead times. Together, these improvements set a new state-of-the-art in global precipitation forecasting.

Authors: James Duncan (University of California, Berkeley); Peter Harrington (Lawrence Berkeley National Laboratory (Berkeley Lab)); Shashank Subramanian (Lawrence Berkeley National Laboratory)

Climate Science & Modeling Extreme Weather Computer Vision & Remote Sensing Generative Modeling
NeurIPS 2022 Learning Surrogates for Diverse Emission Models (Papers Track)
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Abstract: Transportation plays a major role in global CO2 emission levels, a factor that directly connects with climate change. Roadway interventions that reduce CO2 emission levels have thus become a timely requirement. An integral need in assessing the impact of such roadway interventions is access to industry-standard programmatic and instantaneous emission models with various emission conditions such as fuel types, vehicle types, cities of interest, etc. However, currently, there is a lack of well-calibrated emission models with all these properties. Addressing these limitations, this paper presents 1100 programmatic and instantaneous vehicular CO2 emission models with varying fuel types, vehicle types, road grades, vehicle ages, and cities of interest. We hope the presented emission models will facilitate future research in tackling transportation-related climate impact. The released version of the emission models can be found here.

Authors: Edgar Ramirez Sanchez (MIT); Catherine H Tang (Massachusetts Institute of Technology); Vindula Jayawardana (MIT); Cathy Wu (MIT)

Transportation Cities & Urban Planning Data Mining
NeurIPS 2022 Continual VQA for Disaster Response Systems (Papers Track)
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Abstract: Visual Question Answering (VQA) is a multi-modal task that involves answering questions from an input image, semantically understanding the contents of the image and answering it in natural language. Using VQA for disaster management is an important line of research due to the scope of problems that are answered by the VQA system. However, the main challenge is the delay caused by the generation of labels in the assessment of the affected areas. To tackle this, we deployed pre-trained CLIP model, which is trained on visual-image pairs. however, we empirically see that the model has poor zero-shot performance. Thus, we instead use pre-trained embeddings of text and image from this model for our supervised training and surpass previous state-of-the-art results on the FloodNet dataset. We expand this to a continual setting, which is a more real-life scenario. We tackle the problem of catastrophic forgetting using various experience replay methods.

Authors: Aditya Kane (Pune Institute of Computer Technology); V Manushree (Manipal Institute Of Technology); Sahil S Khose (Georgia Institute of Technology)

Disaster Management and Relief Active Learning
NeurIPS 2022 Performance evaluation of deep segmentation models on Landsat-8 imagery (Papers Track)
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Abstract: Contrails, short for condensation trails, are line-shaped ice clouds produced by aircraft engine exhaust when they fly through the cold and humid air. They generate a greenhouse effect by absorbing or directing back to Earth approximately 33% of emitted outgoing longwave radiation. They account for over half of the climate change resulting from aviation activities. Avoiding contrails and adjusting flight routes could be an inexpensive and effective way to reduce their impact. An accurate, automated, and reliable detection algorithm is required to develop and evaluate contrail avoidance strategies. Advancement in contrail detection has been severely limited due to several factors, primarily due to a lack of quality-labelled data. Recently, McCloskey et al. proposed a large human-labelled Landsat-8. Each contrail is carefully labelled with various inputs in various scenes of Landsat-8 satellite imagery. In this work, we benchmark several popular segmentation models with combinations of different loss functions and encoder backbones. This work is the first to apply state-of-the-art segmentation techniques to detect contrails in low-orbit satellite imagery. Our work can also be used as an open benchmark for contrail segmentation.

Authors: Akshat Bhandari (Manipal Institute of Technology, Manipal); Pratinav Seth (Manipal Institute of Technology); Sriya Rallabandi (Manipal Institute of Technology); Aditya Kasliwal (Manipal Institute of Technology); Sanchit Singhal (Manipal Institute of Technology)

Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2022 Synthesis of Realistic Load Data: Adversarial Networks for Learning and Generating Residential Load Patterns (Papers Track)
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Abstract: Responsible energy consumption plays a key role in reducing carbon footprint and CO2 emissions to tackle climate change. A better understanding of the residential consumption behavior using smart meter data is at the heart of the mission, which can inform residential demand flexibility, appliance scheduling, and home energy management. However, access to high-quality residential load data is still limited due to the cost-intensive data collection process and privacy concerns of data shar- ing. In this paper, we develop a Generative Adversarial Network (GAN)-based method to model the complex and diverse residential load patterns and generate synthetic yet realistic load data. We adopt a generation-focused weight selection method to select model weights to address the mode collapse problem and generate diverse load patterns. We evaluate our method using real-world data and demon- strate that it outperforms three representative state-of-the-art benchmark models in better preserving the sequence level temporal dependencies and aggregated level distributions of load patterns.

Authors: Xinyu Liang (Monash University); Hao Wang (Monash University)

Power & Energy Generative Modeling
NeurIPS 2022 Guided Transformer Network for Detecting Methane Emissions in Sentinel-2 Satellite Imagery (Proposals Track)
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Abstract: Methane (CH4) is the chief contributor to global climate change and its mitigation is targeted by the EU, US and jurisdictions worldwide [2]. Recent studies have shown that imagery from the multi-spectral instrument on Sentinel-2 satellites is capable of detecting and estimating large methane emissions. However, most of the current methods rely on temporal relations between a ratio of shortwave-infrared spectra and assume relatively constant ground conditions, and availability of ground information on when there was no methane emission on site. To address such limitations we propose a guided query-based transformer neural network architecture, that will detect and quantify methane emissions without dependence on temporal information. The guided query aspect of our architecture is driven by a Sentinel Enhanced Matched Filter (SEMF) approach, also discussed in this paper. Our network uses all 12 spectral channels of Sentinel-2 imagery to estimate ground terrain and detect methane emissions. No dependence on temporal data makes it more robust to changing ground and terrain conditions and more computationally efficient as it reduces the need to process historical time-series imagery to compute a single date emissions analysis.

Authors: Satish Kumar (University of California, Santa Barbara); William Kingwill (Orbio Earth); Rozanne Mouton (Orbio Earth); Wojciech Adamczyk (ETH, Zurich); Robert Huppertz (Orbio Earth); Evan D Sherwin (Stanford University, Energy and Resources Engineering)

Carbon Capture & Sequestration Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2022 Identification of medical devices using machine learning on distribution feeder data for informing power outage response (Proposals Track)
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Abstract: Power outages caused by extreme weather events due to climate change have doubled in the United States in the last two decades. Outages pose severe health risks to over 4.4 million individuals dependent on in-home medical devices. Data on the number of such individuals residing in a given area is limited. This study proposes a load disaggregation model to predict the number of medical devices behind an electric distribution feeder. This data can be used to inform planning and response to power outages and other emergencies. The proposed solution serves as a measure for climate change adaptation.

Authors: Paraskevi Kourtza (University of Edinburgh); Maitreyee Marathe (University of Wisconsin-Madison); Anuj Shetty (Stanford University); Diego Kiedanski (Yale University)

Power & Energy Disaster Management and Relief Health Time-series Analysis
NeurIPS 2022 Analyzing the global energy discourse with machine learning (Proposals Track)
Abstract and authors: (click to expand)

Abstract: To transform our economy towards net-zero emissions, industrial development of clean energy technologies (CETs) to replace fossil energy technologies (FETs) is crucial. Although the media has great power in influencing consumer behavior and decision making in business and politics, its role in the energy transformation is still underexplored. In this paper, we analyze the global energy discourse via machine learning. For this, we collect a large-scale dataset with ~5 million news articles from seven of the world’s major CO2 emitting countries, covering eight CETs and four FETs. Using machine learning, we then analyze the content of news articles on a highly granular level and along several dimensions, namely relevance (for the energy discourse), context (e.g., costs, regulation, investment), and connotations (e.g., high/increasing vs. low/decreasing costs). By linking empirical discourse patterns to investment and deployment data of CETs and FETs, this study advances the current understanding about the role of the media in the energy transformation. Thereby, it enables businesses, investors, and policy makers to respond more effectively to sensitive topics in the media discourse and leverage windows of opportunity for scaling CETs.

Authors: Malte Toetzke (ETH Zurich); Benedict Probst (ETH Zurich); Yasin Tatar (ETH Zurich); Stefan Feuerriegel (LMU Munich); Volker Hoffmann (ETH Zurich)

Public Policy Behavioral and Social Science Natural Language Processing
NeurIPS 2022 Deep-S2SWind: A data-driven approach for improving Sub-seasonal wind predictions (Proposals Track)
Abstract and authors: (click to expand)

Abstract: A major transformation to mitigate climate change implies a rapid decarbonisation of the energy system and thus, increasing the use of renewable energy sources, such as wind power. However, renewable resources are strongly dependent on local and large-scale weather conditions, which might be influenced by climate change. Weather-related risk assessments are essential for the energy sector, in particular, for power system management decisions, for which forecasts of climatic conditions from several weeks to months (i.e. sub-seasonal scales) are of key importance. Here, we propose a data-driven approach to predict wind speed at longer lead-times that can benefit the energy sector. The main goal of this study is to assess the potential of machine learning algorithms to predict periods of low wind speed conditions that have a strong impact on the energy sector.

Authors: Noelia Otero Felipe (University of Bern); Pascal Horton (University of Bern)

Climate Science & Modeling Earth Observation & Monitoring Interpretable ML Time-series Analysis
NeurIPS 2022 Towards Low Cost Automated Monitoring of Life Below Water to De-risk Ocean-Based Carbon Dioxide Removal and Clean Power (Proposals Track)
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Abstract: Oceans will play a crucial role in our efforts to combat the growing climate emergency. Researchers have proposed several strategies to harness greener energy through oceans and use oceans as carbon sinks. However, the risks these strategies might pose to the ocean and marine ecosystem are not well understood. It is imperative that we quickly develop a range of tools to monitor ocean processes and marine ecosystems alongside the technology to deploy these solutions on a large scale into the oceans. Large arrays of inexpensive cameras placed deep underwater coupled with machine learning pipelines to automatically detect, classify, count and estimate fish populations have the potential to continuously monitor marine ecosystems and help study the impacts of these solutions on the ocean. In this proposal, we discuss the challenges presented by a dark artificially lit underwater video dataset captured 500m below the surface, propose potential solutions to address these challenges, and present preliminary results from detecting and classifying 6 species of fish in deep underwater camera data.

Authors: Kameswari Devi Ayyagari (Dalhousie University); Christopher Whidden (Dalhousie University); Corey Morris (Department of Fisheries and Oceans); Joshua Barnes (National Research Council Canada)

Oceans & Marine Systems Climate Science & Modeling Computer Vision & Remote Sensing
NeurIPS 2022 Towards the Automatic Analysis of Ceilometer Backscattering Profiles using Unsupervised Learning (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Ceilometers use a laser beam to capture certain phenomena in the atmosphere like clouds, precipitation, or aerosol layers. These measurements can be visualized in so-called quick looks that at the moment are mostly analyzed manually by meteorology experts. In this work, we illustrate the path towards the automatic analysis of quick looks by using a hybrid approach combining an image segmentation algorithm with unsupervised representation learning and clustering. We present a first proof of concept and give an outlook on possible future work.

Authors: Michael Dammann (HAW Hamburg); Ina Mattis (Deutscher Wetterdienst); Michael Neitzke (HAW Hamburg); Ralf Möller (University of Lübeck)

Climate Science & Modeling Extreme Weather Computer Vision & Remote Sensing Unsupervised & Semi-Supervised Learning
NeurIPS 2022 Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics (Proposals Track)
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Abstract: Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities. Cargo-bike logistics has been put forward as a high impact candidate for replacing LGVs, with experts estimating over half of urban van deliveries being replaceable by cargo bikes, due to their faster speeds, shorter parking times and more efficient routes across cities. By modelling the relative delivery performance of different vehicle types across urban micro-regions, machine learning can help operators evaluate the business and environmental impact of adding cargo-bikes to their fleets. In this paper, we introduce two datasets, and present initial progress in modelling urban delivery service time (e.g. cruising for parking, unloading, walking). Using Uber’s H3 index to divide the cities into hexagonal cells, and aggregating OpenStreetMap tags for each cell, we show that urban context is a critical predictor of delivery performance.

Authors: Max C Schrader (University of Alabama); Navish Kumar (IIT Kharagpur); Nicolas Collignon (University of Edinburgh); Maria S Astefanoaei (IT University of Copenhagen); Esben Sørig (Kale Collective); Soonmyeong Yoon (Kale Collective); Kai Xu (University of Edinburgh); Akash Srivastava (MIT-IBM)

Cities & Urban Planning Transportation Data Mining Unsupervised & Semi-Supervised Learning
NeurIPS 2022 An Inversion Algorithm of Ice Thickness and InSAR Data for the State of Friction at the Base of the Greenland Ice Sheet (Proposals Track)
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Abstract: With the advent of climate change and global warming, the Greenland Ice Sheet (GrIS) has been melting at an alarming rate, losing over 215 Gt per yr, and accounting for 10% of mean global sea level rise since the 1990s. It is imperative to understand what dynamics are causing ice loss and influencing ice flow in order to successfully project mass changes of ice sheets and associated sea level rise. This work applies machine learning, ice thickness data, and horizontal ice velocity measurements from satellite radar data to quantify the magnitudes and distributions of the basal traction forces that are holding the GrIS back from flowing into the ocean. Our approach uses a hybrid model: InSAR velocity data trains a linear regression model, and these model coefficients are fed into a geophysical algorithm to estimate basal tractions that capture relationships between the ice motion and physical variables. Results indicate promising model performance and reveal significant presence of large basal traction forces around the coastline of the GrIS.

Authors: Aryan Jain (Amador Valley High School); Jeonghyeop Kim (Stony Brook University); William Holt (Stony Brook University)

Climate Science & Modeling Earth Observation & Monitoring Hybrid Physical Models
NeurIPS 2022 Deep learning-based bias adjustment of decadal climate predictions (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Decadal climate predictions are key to inform adaptation strategies in a warming climate. Coupled climate models used for decadal predictions are, however, imperfect representations of the climate system leading to forecast biases. Biases can also result from a poor model initialization that, when combined with forecast drift, can produce errors depending non-linearly on lead time. We propose a deep learning-based bias correction approach for the post-processing of gridded forecasts to enhance the accuracy of decadal predictions.

Authors: Reinel Sospedra-Alfonso (Environment and Climate Change Canada); Johannes Exenberger (Graz University of Technology); Marie C McGraw (Cooperative Institute for Research in the Atmosphere | CIRA); Trung Kien Dang (National University of Singapore)

Climate Science & Modeling
NeurIPS 2022 Surrogate Modeling for Methane Dispersion Simulations Using Fourier Neural Operator (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Methane leak detection and remediation are critical for tackling climate change, where methane dispersion simulations play an important role in emission source attribution. As 3D modeling of methane dispersion is often costly and time-consuming, we train a deep-learning-based surrogate model using the Fourier Neural Operator to learn the PDE solver in our study. Our preliminary result shows that our surrogate modeling provides a fast, accurate and cost-effective solution to methane dispersion simulations, thus reducing the cycle time of methane leak detection.

Authors: Qie Zhang (Microsoft); Mirco Milletari (Microsoft); Yagna Deepika Oruganti (Microsoft); Philipp A Witte (Microsoft)

Climate Science & Modeling Earth Observation & Monitoring
NeurIPS 2022 Detecting Floods from Cloudy Scenes: A Fusion Approach Using Sentinel-1 and Sentinel-2 Imagery (Proposals Track)
Abstract and authors: (click to expand)

Abstract: As the result of climate change, extreme flood events are becoming more frequent. To better respond to such disasters, and to test and calibrate flood models, we need accurate real-world data on flooding extent. Detection of floods from remote sensed imagery suffers from a widespread problem: clouds block flood scenes in images, leading to degraded and fragmented flood datasets. To address this challenge, we propose a workflow based on U-Net, and a dataset that detects flood in cloud-prone areas by fusing information from the Sentinel-1 and Sentinel-2 satellites. The expected result will be a reliable and detailed catalogue of flood extents and how they change through time, allowing us to better understand flooding in different morphological settings and climates.

Authors: Qiuyang Chen (University of Edinburgh); Xenofon Karagiannis (Earth-i Ltd.); Simon M. Mudd (University of Edinburgh)

Computer Vision & Remote Sensing Disaster Management and Relief Extreme Weather Time-series Analysis
NeurIPS 2022 Urban Heat Island Detection and Causal Inference Using Convolutional Neural Networks (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Compared to rural areas, urban areas experience higher temperatures for longer periods of time because of the urban heat island (UHI) effect. This increased heat stress leads to greater mortality, increased energy demand, regional changes to precipitation patterns, and increased air pollution. Urban developers can minimize the UHI effect by incorporating features that promote air flow and heat dispersion (e.g., increasing green space). However, understanding which urban features to implement is complex, as local meteorology strongly dictates how the environment responds to changes in urban form. In this proposal we describe a methodology for estimating the causal relationship between changes in urban form and changes in the UHI effect. Changes in urban form and temperature changes are measured using convolutional neural networks, and a causal inference matching approach is proposed to estimate causal relationships. The success of this methodology will enable urban developers to implement city-specific interventions to mitigate the warming planet's impact on cities.

Authors: Zachary D Calhoun (Duke University); Ziyang Jiang (Duke University); Mike Bergin (Duke University); David Carlson (Duke University)

Cities & Urban Planning Buildings Climate Justice Climate Science & Modeling Disaster Management and Relief Extreme Weather Health Causal & Bayesian Methods Computer Vision & Remote Sensing Time-series Analysis
NeurIPS 2022 Forecasting Global Drought Severity and Duration Using Deep Learning (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Drought detection and prediction is challenging due to the slow onset of the event and varying degrees of dependence on numerous physical and socio-economic factors that differentiate droughts from other natural disasters. In this work, we propose DeepXD (Deep learning for Droughts), a deep learning model with 26 physics-informed input features for SPI (Standardised Precipitation Index) forecasting to identify and classify droughts using monthly oceanic indices, global meteorological and vegetation data, location (latitude, longitude) and land cover for the years 1982 to 2018. In our work, we propose extracting features by considering the atmosphere and land moisture and energy budgets and forecasting global droughts on a seasonal and an annual scale at 1, 3, 6, 9, 12 and 24 months lead times. SPI helps us to identify the severity and the duration of the drought to classify them as meteorological, agricultural and hydrological.

Authors: Akanksha Ahuja (NOA); Xin Rong Chua (Centre for Climate Research Singapore)

Agriculture & Food Climate Science & Modeling Disaster Management and Relief Earth Observation & Monitoring Extreme Weather Societal Adaptation & Resilience Data Mining
NeurIPS 2022 ForestBench: Equitable Benchmarks for Monitoring, Reporting, and Verification of Nature-Based Solutions with Machine Learning (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Restoring ecosystems and reducing deforestation are necessary tools to mitigate the anthropogenic climate crisis. Current measurements of forest carbon stock can be inaccurate, in particular for underrepresented and small-scale forests in the Global South, hindering transparency and accountability in the Monitoring, Reporting, and Verification (MRV) of these ecosystems. There is thus need for high quality datasets to properly validate ML-based solutions. To this end, we present ForestBench, which aims to collect and curate geographically-balanced gold-standard datasets of small-scale forest plots in the Global South, by collecting ground-level measurements and visual drone imagery of individual trees. These equitable validation datasets for ML-based MRV of nature-based solutions shall enable assessing the progress of ML models for estimating above-ground biomass, ground cover, and tree species diversity.

Authors: Lucas Czech (Carnegie Institution for Science); Björn Lütjens (MIT); David Dao (ETH Zurich)

Earth Observation & Monitoring Carbon Capture & Sequestration Climate Finance & Economics Climate Justice Ecosystems & Biodiversity Forests Local and Indigenous Knowledge Systems Public Policy Computer Vision & Remote Sensing Land Use
NeurIPS 2022 Estimating Heating Loads in Alaska using Remote Sensing and Machine Learning Methods (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Alaska and the larger Arctic region are in much greater need of decarbonization than the rest of the globe as a result of the accelerated consequences of climate change over the past ten years. Heating for homes and businesses accounts for over 75% of the energy used in the Arctic region. However, the lack of thorough and precise heating load estimations in these regions poses a significant obstacle to the transition to renewable energy. In order to accurately measure the massive heating demands in Alaska, this research pioneers a geospatial-first methodology that integrates remote sensing and machine learning techniques. Building characteristics such as height, size, year of construction, thawing degree days, and freezing degree days are extracted using open-source geospatial information in Google Earth Engine (GEE). These variables coupled with heating load forecasts from the AK Warm simulation program are used to train models that forecast heating loads on Alaska’s Railbelt utility grid. Our research greatly advances geospatial capability in this area and considerably informs the decarbonization activities currently in progress in Alaska.

Authors: Madelyn Gaumer (University of Washington); Nick Bolten (Paul G. Allen School of Computer Science and Engineering, University of Washington); Vidisha Chowdhury (Heinz College of Information Systems and Public Policy, Carnegie Mellon University); Philippe Schicker (Heinz College of Information Systems and Public Policy, Carnegie Mellon University); Shamsi Soltani (Department of Epidemiology and Population Health, Stanford University School of Medicine); Erin D Trochim (University of Alaska Fairbanks)

Buildings Cities & Urban Planning Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2022 Interpretable Spatiotemporal Forecasting of Arctic Sea Ice Concentration at Seasonal Lead Times (Proposals Track)
Abstract and authors: (click to expand)

Abstract: There are many benefits from the accurate forecasting of Arctic sea ice, however existing models struggle to reliably predict sea ice concentration at long lead times. Many numerical models exist but can be sensitive to initial conditions, and while recent deep learning-based methods improve overall robustness, they either do not utilize temporal trends or rely on architectures that are not performant at learning long-term sequential dependencies. We propose a method of forecasting sea ice concentration using neural circuit policies, a form of continuous time recurrent neural architecture, which improve the learning of long-term sequential dependencies compared to existing techniques and offer the added benefits of adaptability to irregular sequence intervals and high interpretability.

Authors: Matthew Beveridge (Independent Researcher); Lucas Pereira (ITI, LARSyS, Técnico Lisboa)

Computer Vision & Remote Sensing Climate Science & Modeling Earth Observation & Monitoring Oceans & Marine Systems Interpretable ML Time-series Analysis Uncertainty Quantification & Robustness
NeurIPS 2022 CliMedBERT: A Pre-trained Language Model for Climate and Health-related Text (Proposals Track)
Abstract and authors: (click to expand)

Abstract: Climate change is threatening human health in unprecedented orders and many ways. These threats are expected to grow unless effective and evidence-based policies are developed and acted upon to minimize or eliminate them. Attaining such a task requires the highest degree of the flow of knowledge from science into policy. The multidisciplinary, location-specific, and vastness of published science makes it challenging to keep track of novel work in this area, as well as making the traditional knowledge synthesis methods inefficient in infusing science into policy. To this end, we consider developing multiple domain-specific language models (LMs) with different variations from Climate- and Health-related information, which can serve as a foundational step toward capturing available knowledge to enable solving different tasks, such as detecting similarities between climate- and health-related concepts, fact-checking, relation extraction, evidence of health effects to policy text generation, and more. To our knowledge, this is the first work that proposes developing multiple domain-specific language models for the considered domains. We will make the developed models, resources, and codebase available for the researchers.

Authors: Babak Jalalzadeh Fard (University of Nebraska Medical Center); Sadid A. Hasan (Microsoft); Jesse E. Bell (University of Nebraska Medical Center)

Health Natural Language Processing
NeurIPS 2022 Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid (Proposals Track)
Abstract and authors: (click to expand)

Abstract: This proposal aims to develop more accurate federated learning (FL) methods with faster convergence properties and lower communication requirements, specifically for forecasting distributed energy resources (DER) such as renewables, energy storage, and loads in modern, low-carbon power grids. This will be achieved by (i) leveraging recently developed extensions of FL such as hierarchical and iterative clustering to improve performance with non-IID data, (ii) experimenting with different types of FL global models well-suited to time-series data, and (iii) incorporating domain-specific knowledge from power systems to build more general FL frameworks and architectures that can be applied to diverse types of DERs beyond just load forecasting, and with heterogeneous clients.

Authors: Vineet Jagadeesan Nair (MIT)

Power & Energy Buildings Time-series Analysis
NeurIPS 2022 Personalizing Sustainable Agriculture with Causal Machine Learning (Proposals Track) Best Paper: Proposals
Abstract and authors: (click to expand)

Abstract: To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the "sustainable intensification" of agriculture, transforming it from carbon emitter to carbon sink is a priority, and understanding the environmental impact of agricultural management practices is a fundamental prerequisite to that. At the same time, the global agricultural landscape is deeply heterogeneous, with differences in climate, soil, and land use inducing variations in how agricultural systems respond to farmer actions. The "personalization" of sustainable agriculture with the provision of locally adapted management advice is thus a necessary condition for the efficient uplift of green metrics, and an integral development in imminent policies. Here, we formulate personalized sustainable agriculture as a Conditional Average Treatment Effect estimation task and use Causal Machine Learning for tackling it. Leveraging climate data, land use information and employing Double Machine Learning, we estimate the heterogeneous effect of sustainable practices on the field-level Soil Organic Carbon content in Lithuania. We thus provide a data-driven perspective for targeting sustainable practices and effectively expanding the global carbon sink.

Authors: Georgios Giannarakis (National Observatory of Athens); Vasileios Sitokonstantinou (National Observatory of Athens); Roxanne Suzette Lorilla (National Observatory of Athens); Charalampos Kontoes (National Observatory of Athens)

Causal & Bayesian Methods Agriculture & Food Carbon Capture & Sequestration Earth Observation & Monitoring Ecosystems & Biodiversity Public Policy Societal Adaptation & Resilience Data Mining
NeurIPS 2022 Disaster Risk Monitoring Using Satellite Imagery (Tutorials Track)
Abstract and authors: (click to expand)

Abstract: Natural disasters such as flood, wildfire, drought, and severe storms wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems, and economies. Unfortunately, flooding events are on the rise due to climate change and sea level rise. The ability to detect and quantify them can help us minimize their adverse impacts on the economy and human lives. Using satellites to study flood is advantageous since physical access to flooded areas is limited and deploying instruments in potential flood zones can be dangerous. We are proposing a hands-on tutorial to highlight the use of satellite imagery and computer vision to study natural disasters. Specifically, we aim to demonstrate the development and deployment of a flood detection model using Sentinel-1 satellite data. The tutorial will cover relevant fundamental concepts as well as the full development workflow of a deep learning-based application. We will include important considerations such as common pitfalls, data scarcity, augmentation, transfer learning, fine-tuning, and details of each step in the workflow. Importantly, the tutorial will also include a case study on how the application was used by authorities in response to a flood event. We believe this tutorial will enable machine learning practitioners of all levels to develop new technologies that tackle the risks posed by climate change. We expect to deliver the below learning outcomes: • Develop various deep learning-based computer vision solutions using hardware-accelerated open-source tools that are optimized for real-time deployment • Create an optimized pipeline for the machine learning development workflow • Understand different performance metrics for model evaluation that are relevant for real world datasets and data imbalances • Understand the public sector’s efforts to support climate action initiatives and point out where the audience can contribute

Authors: Kevin Lee (NVIDIA); Siddha Ganju (NVIDIA); Edoardo Nemni (UNOSAT)

Disaster Management and Relief Climate Science & Modeling Earth Observation & Monitoring Extreme Weather Active Learning Computer Vision & Remote Sensing Data Mining Meta- and Transfer Learning
NeurIPS 2022 Machine Learning for Predicting Climate Extremes (Tutorials Track)
Abstract and authors: (click to expand)

Abstract: Climate change has led to a rapid increase in the occurrence of extreme weather events globally, including floods, droughts, and wildfires. In the longer term, some regions will experience aridification while others will risk sinking due to rising sea levels. Typically, such predictions are done via weather and climate models that simulate the physical interactions between the atmospheric, oceanic, and land surface processes that operate at different scales. Due to the inherent complexity, these climate models can be inaccurate or computationally expensive to run, especially for detecting climate extremes at high spatiotemporal resolutions. In this tutorial, we aim to introduce the participants to machine learning approaches for addressing two fundamental challenges. We will walk the participants through a hands-on tutorial for predicting climate extremes relating to temperature and precipitation in 2 setups: (1) temporal forecasting: the goal is to predict climate variables into the future (both direct single step approaches and iterative approaches that roll out the model for several timesteps), and (2) spatial downscaling: the goal is to learn a mapping that transforms low-resolution outputs of climate models into high-resolution regional forecasts. Through introductory presentations and colab notebooks, we aim to expose the participants to (a) APIs for accessing and navigating popular repositories that host global climate data, such as the Copernicus data store, (b) identifying relevant datasets, including auxiliary data (e.g., other climate variables such as geopotential), (c) scripts for downloading and preprocessing relevant datasets, (d) algorithms for training machine learning models, (d) metrics for evaluating model performance, and (e) visualization tools for both the dataset and predicted outputs. The coding notebooks will be in Python. No prior knowledge of climate science is required.

Authors: Hritik Bansal (UCLA); Shashank Goel (University of California Los Angeles); Tung Nguyen (University of California, Los Angeles); Aditya Grover (UCLA)

Climate Science & Modeling Earth Observation & Monitoring Extreme Weather Data Mining Hybrid Physical Models
NeurIPS 2022 FourCastNet: A practical introduction to a state-of-the-art deep learning global weather emulator (Tutorials Track)
Abstract and authors: (click to expand)

Abstract: Accurate, reliable, and efficient means of forecasting global weather patterns are of paramount importance to our ability to mitigate and adapt to climate change. Currently, real-time weather forecasting requires repeated numerical simulation and data assimilation cycles on dedicated supercomputers, which restricts the ability to make reliable, high-resolution forecasts to a handful of organizations. However, recent advances in deep learning, specifically the FourCastNet model, have shown that data-driven approaches can forecast important atmospheric variables with excellent skill and comparable accuracy to standard numerical methods, but at orders-of-magnitude lower computational and energy cost during inference, enabling larger ensembles for better probabilistic forecasts. In this tutorial, we demonstrate various applications of FourCastNet for high-resolution global weather forecasting, with examples including real-time forecasts, uncertainty quantification for extreme events, and adaptation to specific variables or localized regions of interest. The tutorial will provide examples that will demonstrate the general workflow for formatting and working with global atmospheric data, running autoregressive inference to obtain daily global forecasts, saving/visualizing model predictions of atmospheric events such as hurricanes and atmospheric rivers, and computing quantitative evaluation metrics for weather models. The exercises will primarily use PyTorch and do not require detailed understanding of the climate and weather system. With this tutorial, we hope to equip attendees with basic knowledge about building deep learning-based weather model surrogates and obtaining forecasts of crucial atmospheric variables using these models.

Authors: Jaideep Pathak (NVIDIA Corporation); Shashank Subramanian (Lawrence Berkeley National Laboratory); Peter Harrington (Lawrence Berkeley National Laboratory (Berkeley Lab)); Thorsten Kurth (Nvidia); Andre Graubner (Nvidia); Morteza Mardani (NVIDIA Corporation); David M. Hall (NVIDIA); Karthik Kashinath (Lawrence Berkeley National Laboratory); Anima Anandkumar (NVIDIA/Caltech)

Climate Science & Modeling Extreme Weather Uncertainty Quantification & Robustness
NeurIPS 2022 Automating the creation of LULC datasets for semantic segmentation (Tutorials Track)
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Abstract: High resolution and accurate Land Use and Land Cover mapping (LULC) datasets are increasingly important and can be widely used in monitoring climate change impacts in agriculture, deforestation, and the carbon cycle. These datasets represent physical classifications of land types and spatial information over the surface of the Earth. These LULC datasets can be leveraged in a plethora of research topics and industries to mitigate and adapt to environmental changes. High resolution urban mappings can be used to better monitor and estimate building albedo and urban heat island impacts, and accurate representation of forests and vegetation can even be leveraged to better monitor the carbon cycle and climate change through improved land surface modelling. The advent of machine learning (ML) based CV techniques over the past decade provides a viable option to automate LULC mapping. One impediment to this has been the lack of large ML datasets. Large vector datasets for LULC are available, but can’t be used directly by ML practitioners due to a knowledge gap in transforming the input into a dataset of paired satellite images and segmentation masks. We demonstrate a novel end-to-end pipeline for LULC dataset creation that takes vector land cover data and provides a training-ready dataset. We will use Sentinel-2 satellite imagery and the European Urban Atlas LULC data. The pipeline manages everything from downloading satellite data, to creating and storing encoded segmentation masks and automating data checks. We then use the resulting dataset to train a semantic segmentation model. The aim of the pipeline is to provide a way for users to create their own custom datasets using various combinations of multispectral satellite and vector data. In addition to presenting the pipeline, we aim to provide an introduction to multispectral imagery, geospatial data and some of the challenges in using it for ML.

Authors: Sambhav S Rohatgi (Spacesense.ai); Anthony Mucia (Spacesense.ai)

Computer Vision & Remote Sensing Cities & Urban Planning Climate Science & Modeling Forests Land Use
AAAI FSS 2022 AI-Based Text Analysis for Evaluating Food Waste Policies
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Abstract: Food waste is a major contributor to climate change, making the reduction of food waste one of the most important strategies to preserve threatened ecosystems and increase economic benefits. To evaluate the impact of food waste policies in this arena and provide actionable guidance to policymakers, we conducted an AI-based text analysis of food waste policy provisions. Specifically, we a) identified commonalities across state policy texts, b) clustered states by shared policy text, and c) examined relationships between state cluster memberships and food waste . This approach generated state clusters but demonstrated very limited convergent validity with policy ratings provided by subject matter experts and no predictive validity with food waste. We discuss the potential of using supervised machine learning to analyze food waste policy text as a next step.

Authors: John Aitken (The MITRE Corporation), Denali Rao (The MITRE Corporation), Balca Alaybek (The MITRE Corporation), Amber Sprenger (The MITRE Corporation), Grace Mika (The MITRE Corporation), Rob Hartman (The MITRE Corporation) and Laura Leets (The MITRE Corporation)

Natural Language Processing Agriculture & Food
AAAI FSS 2022 Data-Driven Reduced-Order Model for Atmospheric CO2 Dispersion
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Abstract: Machine learning frameworks have emerged as powerful tools for the enhancement of computational fluid dynamics simulations and the construction of reduced-order models (ROMs). The latter are particularly desired when their full-order counterparts portray multiple spatiotemporal features and demand high processing power and storage capacity, such as climate models. In this work, a ROM for CO2 dispersion across Earth‘s atmosphere was built from NASA’s gridded daily OCO-2 carbon dioxide assimilated dataset. For that, a proper orthogonal decomposition was performed, followed by a non-intrusive operator inference (OpInf). This scientific machine learning technique was capable of accurately representing and predicting the detailed CO2 concentration field for about one year ahead, with a normalized root-mean-square error below 5%. It suggests OpInf-based ROMs may be a reliable alternative for fast response climate-related predictions.

Authors: Pedro Roberto Barbosa Rocha (IBM Research), Marcos Sebastião de Paula Gomes (Pontifical Catholic University of Rio de Janeiro), João Lucas de Sousa Almeida (IBM Research), Allan Moreira Carvalho (IBM Research) and Alberto Costa Nogueira Junior (IBM Research)

Climate Science & Modeling
AAAI FSS 2022 KnowUREnvironment: An Automated Knowledge Graph for Climate Change and Environmental Issues
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Abstract: Despite climate change being one of the greatest threats to humanity, many people are still in denial or lack motivation for appropriate action. A structured source of knowledge can help increase public awareness while also helping crucial natural language understanding tasks such as information retrieval, question answering, and recommendation systems. We introduce KnowUREnvironment – a knowledge graph for climate change and related environmental issues, extracted from the scientific literature. We automatically identify 210,230 domain-specific entities/concepts and encode how these concepts are interrelated with 411,860 RDF triples backed up with evidence from the literature, without using any supervision or human intervention. Human evaluation shows our extracted triples are syntactically and factually correct (81.69% syntactic correctness and 75.85% precision). The proposed framework can be easily extended to any domain that can benefit from such a knowledge graph.

Authors: Md Saiful Islam (University of Rochester), Adiba Proma (University of Rochester), Yilin Zhou (University of Rochester), Syeda Nahida Akter (Carnegie Mellon University), Caleb Wohn (University of Rochester) and Ehsan Hoque (University of Rochester)

Natural Language Processing
AAAI FSS 2022 Towards Generating Large Synthetic Phytoplankton Datasets for Efficient Monitoring of Harmful Algal Blooms
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Abstract: Climate change is increasing the frequency and severity of harmful algal blooms (HABs), which cause significant fish deaths in aquaculture farms. This contributes to ocean pollution and greenhouse gas (GHG) emissions since dead fish are either dumped into the ocean or taken to landfills, which in turn negatively impacts the climate. Currently, the standard method to enumerate harmful algae and other phytoplankton is to manually observe and count them under a microscope. This is a time-consuming, tedious and error-prone process, resulting in compromised management decisions by farmers. Hence, automating this process for quick and accurate HAB monitoring is extremely helpful. However, this requires large and diverse datasets of phytoplankton images, and such datasets are hard to produce quickly. In this work, we explore the feasibility of generating novel high-resolution photorealistic synthetic phytoplankton images, containing multiple species in the same image, given a small dataset of real images. To this end, we employ Generative Adversarial Networks (GANs) to generate synthetic images. We evaluate three different GAN architectures: ProjectedGAN, FastGAN, and StyleGANv2 using standard image quality metrics. We empirically show the generation of high-fidelity synthetic phytoplankton images using a training dataset of only 961 real images. Thus, this work demonstrates the ability of GANs to create large synthetic datasets of phytoplankton from small training datasets, accomplishing a key step towards sustainable systematic monitoring of harmful algal blooms.

Authors: Nitpreet Bamra (University of Waterloo), Vikram Voleti (Mila, University of Montreal), Alexander Wong (University of Waterloo) and Jason Deglint (University of Waterloo)

Generative Modeling Computer Vision & Remote Sensing Oceans & Marine Systems Ecosystems & Biodiversity
AAAI FSS 2022 Generating physically-consistent high-resolution climate data with hard-constrained neural networks
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Abstract: The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often predict quantities at a coarse spatial resolution. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often using methods from the super-resolution domain in computer vision. Despite often achieving visually compelling results, such models often violate conservation laws when predicting physical variables. In order to conserve important physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep downscaling model while also increasing their performance according to traditional metrics. We introduce two ways of constraining the network: a renormalization layer added to the end of the neural network and a successive approach that scales with increasing upsampling factors. We show the applicability of our methods across different popular architectures and upsampling factors using ERA5 reanalysis data.

Authors: Paula Harder (Fraunhofer Institute ITWM, Mila Quebec AI Institute), Qidong Yang (Mila Quebec AI Institute, New York University), Venkatesh Ramesh (Mila Quebec AI Institute, University of Montreal), Alex Hernandez-Garcia (Mila Quebec AI Institute, University of Montreal), Prasanna Sattigeri (IBM Research), Campbell D. Watson (IBM Research), Daniela Szwarcman (IBM Research) and David Rolnick (Mila Quebec AI Institute, McGill University).

Climate Science & Modeling Weather & Climate Hybrid Physical Models
AAAI FSS 2022 Discovering Transition Pathways Towards Coviability with Machine Learning
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Abstract: This paper presents our ongoing French-Brazilian collaborative project which aims at: (1) establishing a diagnosis of socio-ecological coviability for several sites of interest in Nordeste, the North-East region of Brazil (in the states of Paraiba, Ceara, Pernambuco, and Rio Grande do Norte known for their biodiversity hotspots and vulnerabilities to climate change) using advanced data science techniques for multisource and multimodal data fusion and (2) finding transition pathways towards coviability equilibrium using machine learning techniques. Data collected in the field by scientists, ecologists, local actors combined with volunteered information, pictures from smart-phones, and data available on-line from satellite imagery, social media, surveys, etc. can be used to compute various coviability indicators of interest for the local actors. These indicators are useful to characterize and monitor the socio-ecological coviability status along various dimensions of anthropization, human welfare, ecological and biodiversity balance, and ecosystem intactness and vulnerabilities.

Authors: Laure Berti-Equille (IRD) and Rafael Raimundo (UFPB)

Local and Indigenous Knowledge Systems Ecosystems & Biodiversity Reinforcement Learning Computer Vision & Remote Sensing
AAAI FSS 2022 Wildfire Forecasting with Satellite Images and Deep Generative Model
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Abstract: Wildfire prediction has been one of the most critical tasks that humanities want to thrive at. While it plays a vital role in protecting human life, it is also difficult because of its stochastic and chaotic properties. We tackled the problem by interpreting a series of wildfire images into a video and used it to anticipate how the fire would behave in the future. However, creating video prediction models that account for the inherent uncertainty of the future is challenging. The bulk of published attempts are based on stochastic image-autoregressive recurrent networks, which raise various performance and application difficulties such as computational cost and limited efficiency on massive datasets. Another possibility is to use entirely latent temporal models that combine frame synthesis with temporal dynamics. However, due to design and training issues, no such model for stochastic video prediction has yet been proposed in the literature. This paper addresses these issues by introducing a novel stochastic temporal model whose dynamics are driven in a latent space. It naturally predicts video dynamics by allowing our lighter, more interpretable latent model to beat previous state-of-the-art approaches on the GOES-16 dataset. Results are compared using various benchmarking models.

Authors: Thai-Nam Hoang (University of Wisconsin - Madison), Sang Truong (Stanford University) and Chris Schmidt (University of Wisconsin - Madison)

Computer Vision & Remote Sensing Generative Modeling Disaster Management and Relief
AAAI FSS 2022 From Ideas to Deployment - A Joint Industry-University Research Effort on Tackling Carbon Storage Challenges with AI
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Abstract: Carbon capture and storage (CCS) offers a promising means for significant reductions in greenhouse gas emissions and climate change mitigation at a large scale. Modeling CO2 transport and pressure buildup is central to understanding the responses of geosystems after CO2 injection and assessing the suitability and safety of CO2 storage. However, numerical simulations of geological CO2 storage often suffer from its multi-physics nature and complex non-linear governing equations, and is further complicated by flexible injection designs including changes in injection rates, resulting in formidable computational costs. New ideas have emerged such as data-driven models to tackle such challenges but very few have been fully developed and deployed as reliable tools. With the joint efforts of industry and universities, we are currently working on a new mechanism of fostering cross-disciplinary collaboration, developing, deploying, and scaling data-driven tools for CCS. A deep learning suite that can act as an alternative to CCS variable rate injection simulation will be the first tool developed under this mechanism. Based on the surrogate model, optimal design of injection strategy under pressure buildup constraints will be enabled with machine learning.

Authors: Junjie Xu (Tsinghua University), Jiesi Lei (Tsinghua University), Yang Li (Tsinghua University), Junfan Ren (College of Petroleum Engineering, China University of Petroleum (Beijing), Beijing, China), Jian Qiu (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China), Biao Luo (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China), Lei Xiao (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China) and Wenwen Zhou (Product and Solution & Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, China)

Carbon Capture & Sequestration
AAAI FSS 2022 NADBenchmarks - a compilation of Benchmark Datasets for Machine Learning Tasks related to Natural Disasters
Abstract and authors: (click to expand)

Abstract: Climate change has increased the intensity, frequency, and duration of extreme weather events and natural disasters across the world. While the increased data on natural disasters improves the scope of machine learning(ML) for this field, progress is relatively slow. One bottleneck is the lack of benchmark datasets that would allow ML researchers to quantify their progress against a standard metric. The objective of this short paper is to explore the state of benchmark datasets for ML tasks related to natural disasters, categorizing the datasets according to the disaster management cycle. We compile a list of existing benchmark datasets that have been introduced in the past five years. We propose a web platform where researchers can search for benchmark datasets in this domain, and develop a preliminary version of such a platform using our compiled list. This paper is intended to aid researchers in finding benchmark datasets to train their ML models on, and provide general directions in for topics where they can contribute new benchmark datasets.

Authors: Adiba Proma (University of Rochester), Md Saiful Islam (University of Rochester), Stela Ciko (University of Rochester), Raiyan Abdul Baten (University of Rochester) and Ehsan Hoque (University of Rochester)

Disaster Management and Relief Data Mining
AAAI FSS 2022 Contrastive Learning for Climate Model Bias Correction and Super-Resolution
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Abstract: Climate models often require post-processing in order to make accurate estimates of local climate risk. The most common post-processing applied is bias-correction and spatial resolution enhancement. However, the statistical methods typically used for this not only are incapable of capturing multivariate spatial correlation information but are also reliant on rich observational data often not available outside of developed countries, limiting their potential. Here we propose an alternative approach to this challenge based on a combination of image super resolution (SR) and contrastive learning generative adversarial networks (GANs). We benchmark performance against NASA’s flagship post-processed CMIP6 climate model product, NEX-GDDP. We find that our model successfully reaches a spatial resolution double that of NASA’s product while also achieving comparable or improved levels of bias correction in both daily precipitation and temperature. The resulting higher fidelity simulations of present and forward-looking climate can enable more local, accurate models of hazards like flooding, drought, and heatwaves.

Authors: Tristan Ballard (Sust Global) and Gopal Erinjippurath (Sust Global)

Climate Science & Modeling Generative Modeling
AAAI FSS 2022 Employing Deep Learning to Quantify Power Plant Greenhouse Gas Emissions via Remote Sensing Data
Abstract and authors: (click to expand)

Abstract: Greenhouse gasses (GHG) emitted from fossil-fuel-burning power plants pose a global threat to climate and public health. GHG emissions degrade air quality and increase the frequency of natural disasters five-fold, causing 8.7 million deaths per year. Quantifying GHG emissions is crucial for the success of the planet. However, current methods to track emissions cost upwards of $520,000/plant. These methods are cost prohibitive for developing countries, and are not globally standardized, leading to inaccurate emissions reports from nations and companies. I developed a low-cost solution via an end-to-end deep learning pipeline that utilizes observations of emitted smoke plumes in satellite imagery to provide an accurate, precise system for quantifying power plant GHG emissions by segmentation of power plant smoke plumes, classification of the plant fossil fuels, and algorithmic prediction of power generation and CO2 emissions. The pipeline was able to achieve a segmentation Intersection Over Union (IoU) score of 0.841, fuel classification accuracy of 92%, and quantify power generation and CO2 emission rates with R2 values of 0.852 and 0.824 respectively. The results of this work serve as a step toward the low-cost monitoring and detection of major sources of GHG emissions, helping limit their catastrophic impacts on climate and our planet.

Authors: Aryan Jain (Amador Valley High School)

Computer Vision & Remote Sensing Earth Observation & Monitoring
AAAI FSS 2022 ClimateBert: A Pretrained Language Model for Climate-Related Text
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Abstract: Over the recent years, large pretrained language models (LM) have revolutionized the field of natural language processing (NLP). However, while pretraining on general language has been shown to work very well for common language, it has been observed that niche language poses problems. In particular, climate-related texts include specific language that common LMs can not represent accurately. We argue that this shortcoming of today's LMs limits the applicability of modern NLP to the broad field of text processing of climate-related texts. As a remedy, we propose ClimateBert, a transformer-based language model that is further pretrained on over 2 million paragraphs of climate-related texts, crawled from various sources such as common news, research articles, and climate reporting of companies. We find that ClimateBert leads to a 48% improvement on a masked language model objective which, in turn, leads to lowering error rates by 3.57% to 35.71% for various climate-related downstream tasks like text classification, sentiment analysis, and fact-checking

Authors: Nicolas Webersinke (FAU Erlangen-Nürnberg), Mathias Kraus (FAU Erlangen-Nürnberg), Julia Anna Bingler (ETH Zurich) and Markus Leippold (UZH Zurich)

Natural Language Processing
AAAI FSS 2022 Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning
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Abstract: Applying Machine learning to domains like Earth Sciences is impeded by the lack of labeled data, despite a large corpus of raw data available in such domains. For instance, training a wildfire classifier on satellite imagery requires curating a massive and diverse dataset, which is an expensive and time-consuming process that can span from weeks to months. Searching for relevant examples in over 40 petabytes of unlabelled data requires researchers to manually hunt for such images, much like finding a needle in a haystack. We present a no-code end-to-end pipeline, Curator, which dramatically minimizes the time taken to curate an exhaustive labeled dataset. Curator is able to search massive amounts of unlabelled data by combining self-supervision, scalable nearest neighbor search, and active learning to learn and differentiate image representations. The pipeline can also be readily applied to solve problems across different domains. Overall, the pipeline makes it practical for researchers to go from just one reference image to a comprehensive dataset in a diminutive span of time.

Authors: Tarun Narayanan (SpaceML), Ajay Krishnan (SpaceML), Anirudh Koul (Pinterest, SpaceML, FDL) and Siddha Ganju (NVIDIA, SpaceML, FDL)

Data Mining Computer Vision & Remote Sensing
AAAI FSS 2022 De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images
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Abstract: With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO2 storage has shown promising results in its ability to monitor the growth of the CO2 plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO2 concentration, additional developments are required to efficiently interpret the seismic images for leakage. In this work, we introduce a binary classification of time-lapse seismic images to delineate CO2 plumes (leakage) using state-of-the-art deep learning models. Additionally, we localize the leakage region of CO2 plumes by leveraging Class Activation Mapping (CAM) methods.

Authors: Huseyin Tuna Erdinc (Georgia Institute of Technology), Abhinav Prakash Gahlot (Georgia Institute of Technology), Ziyi Yin (Georgia Institute of Technology), Mathias Louboutin (Georgia Institute of Technology) and Felix J. Herrmann (Georgia Institute of Technology)

Carbon Capture & Sequestration Interpretable ML Computer Vision & Remote Sensing
AAAI FSS 2022 Predicting Wildfire Risk Under Novel 21st-Century Climate Conditions
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Abstract: Wildfires are one of the most impactful hazards associated with climate change, and in a hotter, drier world, wildfires will be much more common than they have historically been. However, the exact severity and frequency of future wildfires are difficult to estimate, because climate change will create novel combinations of vegetation and fire weather outside what has been historically observed. This provides a challenge for AI-based approaches to long-term fire risk modeling, as much future fire risk is outside of the available feature space provided by the historical record. Here, we give an overview of this problem that is inherent to many climate change impacts and propose a restricted model form that makes monotonic and interpretable predictions in novel fire weather environments. We then show how our model outperforms other neural networks and logistic regression models when making predictions on unseen data from a decade into the future.

Authors: Matthew Cooper (Sust Global).

Disaster Management and Relief Interpretable ML Uncertainty Quantification & Robustness
AAAI FSS 2022 Probabilistic Machine Learning in Polar Earth and Climate Science: A Review of Applications and Opportunities
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Abstract: Our world’s climate future is on thin ice. The study of longterm weather patterns in the polar regions is an important building block in tackling Climate Change. Our understanding of the past, the present and the future of the earth system, and the inherent uncertainty, informs planning, mitigation, and adaptation strategies. In this work we review previous applications of machine learning and statistical computing to polar climate research, and we highlight promising probabilistic machine learning methods that address the modelling needs of climate-related research in the Arctic and the Antarctic. We discuss common challenges in this interdisciplinary field and provide an overview of opportunities for future work in this novel area of research.

Authors: Kim Bente (The University of Sydney), Judy Kay (The University of Sydney) and Roman Marchant (Commonwealth Scientific and Industrial Research Organisation (CSIRO))

Climate Science & Modeling Uncertainty Quantification & Robustness
AAAI FSS 2022 Rethinking Machine Learning for Climate Science: A Dataset Perspective
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Abstract: The growing availability of data sources is a predominant factor enabling the widespread success of machine learning (ML) systems across a wide range of applications. Typically, training data in such systems constitutes a source of ground-truth, such as measurements about a physical object (e.g., natural images) or a human artifact (e.g., natural language). In this position paper, we take a critical look at the validity of this assumption for datasets for climate science. We argue that many such climate datasets are uniquely biased due to the pervasive use of external simulation models (e.g., general circulation models) and proxy variables (e.g., satellite measurements) for imputing and extrapolating in-situ observational data. We discuss opportunities for mitigating the bias in the training and deployment of ML systems using such datasets. Finally, we share views on improving the reliability and accountability of ML systems for climate science applications.

Authors: Aditya Grover (UCLA)

Data Mining
AAAI FSS 2022 Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints
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Abstract: To meet the mid-century goal of CO2 emissions reduction requires a rapid transformation of current electric power and natural gas (NG) infrastructure. This necessitates a long-term planning of the joint power-NG system under representative demand patterns, operational constraints, and policy considerations. Our work is motivated by the computational and practical challenges associated with solving the generation and transmission expansion problem (GTEP) for joint planning of power-NG systems. Specifically, we focus on efficiently extracting a set of representative days from power and NG demand data in respective networks and using this set to reduce the computational burden required to solve the GTEP. We propose a Graph Autoencoder for Multiple time resolution Energy Systems (GAMES) to capture the spatio-temporal demand patterns in interdependent networks and account for differences in the temporal resolution of available data. The resulting embeddings are used in a clustering algorithm to select representative days. We evaluate the effectiveness of our approach in solving a GTEP formulation calibrated for the joint power-NG system in New England. This formulation accounts for the physical interdependencies between power and NG systems, including the joint emissions constraint. Our results show that the set of representative days obtained from GAMES not only allows us to tractably solve the GTEP formulation, but also achieves a lower cost of implementing the joint planning decisions.

Authors: Aron Brenner (MIT), Rahman Khorramfar (MIT), Dharik Mallapragada (MIT) and Saurabh Amin (MIT)

Power & Energy Time-series Analysis Optimization
AAAI FSS 2022 Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N
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Abstract: Comprehensive global cooperation is essential to limit global temperature increases while continuing economic development, e.g., reducing severe inequality or achieving long-term economic growth. Achieving long-term cooperation on climate change mitigation with n strategic agents poses a complex game-theoretic problem. For example, agents may negotiate and reach climate agreements, but there is no central authority to enforce adherence to those agreements. Hence, it is critical to design negotiation and agreement frameworks that foster cooperation, allow all agents to meet their individual policy objectives, and incentivize long-term adherence. This is an interdisciplinary challenge that calls for collaboration between researchers in machine learning, economics, climate science, law, policy, ethics, and other fields. In particular, we argue that machine learning is a critical tool to address the complexity of this domain. To facilitate this research, here we introduce RICE-N, a multi-region integrated assessment model that simulates the global climate and economy, and which can be used to design and evaluate the strategic outcomes for different negotiation and agreement frameworks. We also describe how to use multi-agent reinforcement learning to train rational agents using RICE-N. This framework underpins AI for Global Climate Cooperation, a working group collaboration and competition on climate negotiation and agreement design. Here, we invite the scientific community to design and evaluate their solutions using RICE-N, machine learning, economic intuition, and other domain knowledge. More information can be found on www.ai4climatecoop.org.

Authors: Tianyu Zhang (Université de Montréal, MILA), Andrew Williams (Université de Montréal, MILA), Soham Phade (Salesforce Research), Sunil Srinivasa (Salesforce Research), Yang Zhang (MILA), Prateek Gupta (MILA, University of Oxford, The Alan Turing Institute), Yoshua Bengio (Université de Montréal, MILA, CIFAR) and Stephan Zheng (Salesforce Research)

Impact Assessment Public Policy
AAAI FSS 2022 Self-Supervised Representations of Geo-located Weather Time Series - an Evaluation and Analysis
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Abstract: Self-supervised learning (SSL) is gaining traction in various domains, and demonstrated their potential particularly in tasks where labelled data is limited and costly to collect. In this work, we evaluate the performance existing self-supervised multivariate time series learning algorithms on weather-driven applications involving regression, classification and forecasting tasks. We experiment with a two-step protocol. In the first step, we employ and SSL algorithm and learn generic weather representations from multivariate weather data. Then, in the next step, we use these representations and fine-tune them for multiple downstream tasks. Through our initial experiments on air quality prediction and renewable energy generation tasks, we highlight the benefits of self-supervised weather representations, including improved performance in such tasks, ability to generalize with limited in-task data, and reduction in training time and carbon emissions. We expect such a direction to be relevant in multiple weather-driven applications supporting climate change mitigation and adaptation efforts.

Authors: Arjun Ashok (IBM Research), Devyani Lambhate (IBM Research) and Jitendra Singh (IBM Research)

Weather & Climate Time-series Analysis
AAAI FSS 2022 Predicting Daily Ozone Air Pollution With Transformers
Abstract and authors: (click to expand)

Abstract: Surface ozone is an air pollutant that contributes to hundreds of thousands of premature deaths annually. Ozone at the surface also has considerable negative impacts on vegetation and crop yields. Ozone concentrations are affected by environmental factors, including temperature, which means that ozone concentrations are likely to change in future climates, posing risks to human health. This effect is known as the ozone climate penalty, and recent work suggests that currently polluted areas are likely to become more polluted by ozone in future climates. In light of recent stricter WHO regulations on surface ozone concentrations, we aim to build a predictive data-driven model for recent ozone concentrations, which could be used to make predictions of ozone concentrations in future climates, better quantifying future risks to human health and gaining insight into the variables driving ozone concentrations. We use observational station data from three European countries to train a transformer-based model to make predictions of daily maximum 8-hour ozone.

Authors: Sebastian Hickman (University of Cambridge), Paul Griffiths (University of Cambridge), Peer Nowack (University of East Anglia) and Alex Archibald (University of Cambridge)

Weather & Climate
AAAI FSS 2022 The Impact of TCFD Reporting - A New Application of Zero-Shot Analysis to Climate-Related Financial Disclosures
Abstract and authors: (click to expand)

Abstract: We examine climate-related disclosures in 3,335 reports based on a sample of 188 banks that officially endorsed the recommendations of the Task Force for Climate-related Financial Disclosures (TCFD). In doing so, we introduce a new application of zero-shot text classification based on the BART model and a MNLI task. By developing a set of robust and fine-grained labels, we show that zero-shot analysis provides high accuracy in analyzing companies’ climate-related reporting without further model training. We are able to demonstrate that banks that support the TCFD increase their level of disclosure after officially declaring their support for the guidelines, although we also find significant differences depending on the topic of disclosure. Our findings yield important conclusions for the design of climate-related disclosures.

Authors: Alix Auzepy (Justus-Liebig-Universität Gießen), Elena Tönjes (Justus-Liebig-Universität Gießen) and Christoph Funk (Justus-Liebig-Universität Gießen)

Climate Finance & Economics Natural Language Processing
AAAI FSS 2022 Using Natural Language Processing for Automating the Identification of Climate Action Interlinkages within the Sustainable Development Goals
Abstract and authors: (click to expand)

Abstract: Climate action, Goal 13 of the UN Sustainable Development Goals (SDG), cuts across almost all SDGs. Achieving climate goals can reinforce the achievements in many other goals, but at the same time climate mitigation and adaptation measures may generate trade-offs, such as levelling the cost of energy and transitioning away from fossil fuels. Leveraging the synergies and minimizing the trade-offs among the climate goals and other SDGs is an imperative task for ensuring policy coherence. Understanding the interlinkages between climate action and other SDGs can help inform about the synergies and trade-offs. This paper presents a novel methodology by using natural language processing (NLP) to automate the process of systematically identifying the key interlinkages between climate action and SDGs from a large amount of climate literature. A qualitative SDG interlinkages model for climate action was automatically generated and visualized in a network graph. This work contributes to the conference thematic topic on using AI for policy alignment for climate change goals, SDGs and associated environmental, social and governance (ESG) frameworks.

Authors: Xin Zhou (Institute for Global Environmental Strategies (IGES)), Kshitij Jain (Google Inc.), Mustafa Moinuddin (Institute for Global Environmental Strategies (IGES)) and Patrick McSharry (Carnegie Mellon University Africa; Oxford Man Institute of Quantitative Finance, Oxford University)

Natural Language Processing Climate Policy
AAAI FSS 2022 Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic
Abstract and authors: (click to expand)

Abstract: Streamflow observation data is vital for flood monitoring, agricultural, and settlement planning. However, such streamflow data are commonly plagued with missing observations due to various causes such as harsh environmental conditions and constrained operational resources. This problem is often more pervasive in under-resourced areas such as Sub-Saharan Africa. In this work, we reconstruct streamflow time series data through bias correction of the GEOGloWS ECMWF streamflow service (GESS) forecasts at ten river gauging stations in Benin. We perform bias correction by fitting Quantile Mapping, Gaussian Process, and Elastic Net regression in a constrained training period. We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in low predictive skill over the ten Beninese stations. Our findings suggest that overall bias correction by Elastic Net and Gaussian Process regression achieves superior skill relative to traditional imputation by Random Forest, k-Nearest Neighbour, and GESS lookup. The findings of this work provide a basis for integrating global GESS streamflow data into operational early-warning decision-making systems (e.g., flood alert) in countries vulnerable to drought and flooding due to extreme weather events.

Authors: Rendani Mbuvha (Queen Mary University of London), Julien Yise Peniel Adounkpe (International Water Management Institute (IWMI)), Wilson Tsakane Mongwe (University of Johannesburg), Mandela Houngnibo (Agence Nationale de la Météorologie du Benin Meteo Benin), Nathaniel Newlands (Summerland Research and Development Centre, Agriculture and Agri-Food Canada) and Tshilidzi Marwala (University of Johannesburg)

Extreme Weather Forecasting
AAAI FSS 2022 Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network
Abstract and authors: (click to expand)

Abstract: Geostationary satellite imagery has applications in climate and weather forecasting, planning natural energy resources, and predicting extreme weather events. For precise and accurate prediction, higher spatial and temporal resolution of geostationary satellite imagery is important. Although recent geostationary satellite resolution has improved, the long-term analysis of climate applications is limited to using multiple satellites from the past to the present due to the different resolutions. To solve this problem, we proposed warp and refine network (WR-Net). WR-Net is divided into an optical flow warp component and a warp image refinement component.We used the TV-L1 algorithm instead of deep learning-based approaches to extract the optical flow warp component. The deep-learning-based model is trained on the human-centric view of the RGB channel and does not work on geostationary satellites, which is gray-scale one-channel imagery.The refinement network refines the warped image through a multi-temporal fusion layer. We evaluated WR-Net by interpolation of temporal resolution at 4 min intervals to 2 min intervals in large-scale GK2A geostationary meteorological satellite imagery. Furthermore, we applied WR-Net to the future frame prediction task and showed that the explicit use of optical flow can help future frame prediction.

Authors: Minseok Seo (SI Analytics), Yeji Choi (SI Analytics), Hyungon Ryu (NVIDIA), Heesun Park (National Institute of Meteorological Science), Hyungkun Bae (SI Analytics), Hyesook Lee (National Institute of Meteorological Science) and Wanseok Seo (NVIDIA)

Computer Vision & Remote Sensing
AAAI FSS 2022 Machine Learning Methods in Climate Finance: A Systematic Review
Abstract and authors: (click to expand)

Abstract: Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the potential for the use of ML in climate finance and the proliferation of articles in this field, we propose a survey of the academic literature to assess how ML is enabling climate finance to scale up. The contribution of this paper is threefold. First, we do a systematic search in three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular application domains where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Secondly, we do an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area, aiming to spur further innovative work from ML experts.

Authors: Andres Alonso-Robisco (Banco de España), Jose Manuel Carbo (Banco de España) and Jose Manuel Marques (Banco de España)

Climate Finance & Economics
NeurIPS 2021 Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning (Papers Track)
Abstract and authors: (click to expand)

Abstract: Floods wreak havoc throughout the world, causing billions of dollars in damages, and uprooting communities, ecosystems and economies. The NASA Impact Emerging Techniques in Computational Intelligence (ETCI) competition on Flood Detection tasked participants with predicting flooded pixels after training with synthetic aperture radar (SAR) images in a supervised setting. We propose a semi-supervised learning pseudo-labeling scheme that derives confidence estimates from U-Net ensembles, thereby progressively improving accuracy. Concretely, we use a cyclical approach involving multiple stages (1) training an ensemble model of multiple U-Net architectures with the provided high confidence hand-labeled data and, generated pseudo labels or low confidence labels on the entire unlabeled test dataset, and then, (2) filter out quality generated labels and, (3) combine the generated labels with the previously available high confidence hand-labeled dataset. This assimilated dataset is used for the next round of training ensemble models. This cyclical process is repeated until the performance improvement plateaus. Additionally, we post process our results with Conditional Random Fields. Our approach sets a high score, and a new state-of-the-art on the Sentinel-1 dataset for the ETCI competition with 0.7654 IoU, an impressive improvement over the 0.60 IOU baseline. Our method, which we release with all the code including trained models, can also be used as an open science benchmark for the Sentinel-1 released dataset.

Authors: Siddha Ganju (Nvidia Corporation); Sayak Paul (Carted)

Unsupervised & Semi-Supervised Learning Disaster Management and Relief Earth Observation & Monitoring Computer Vision & Remote Sensing
NeurIPS 2021 Short-term Solar Irradiance Prediction from Sky Images (Papers Track)
Abstract and authors: (click to expand)

Abstract: Solar irradiance forecasting is essential for the integration of the solar power into the power grid system while maintaining its stability. This paper focuses on short-term solar irradiance forecasting (upto 4-hour ahead-of-time prediction) from a past sky image sequence. Most existing work aims for the prediction of the most likely future of the solar irradiance. While it is likely deterministic for intra-hourly prediction, the future solar irradiance is naturally diverse over a relatively long-term horizon (>1h). We therefore introduce approaches to deterministic and stochastic predictions to capture the most likely as well as the diverse future of the solar irradiance. To enable the autoregressive prediction capability of the model, we proposed deep neural networks to predict the future sky images in a deterministic as well as stochastic manner. We evaluate our approaches on benchmark datasets and demonstrate that our approaches achieve superior performance.

Authors: Hoang Chuong Nguyen (Australia National University); Miaomiao Liu (The Australian National University)

Computer Vision & Remote Sensing Power & Energy
NeurIPS 2021 Towards Representation Learning for Atmospheric Dynamics (Papers Track)
Abstract and authors: (click to expand)

Abstract: The prediction of future climate scenarios under anthropogenic forcing is critical to understand climate change and to assess the impact of potentially counter-acting technologies. Machine learning and hybrid techniques for this prediction rely on informative metrics that are sensitive to pertinent but often subtle influences. For atmospheric dynamics, a critical part of the climate system, no well established metric exists and visual inspection is currently still often used in practice. However, this ``eyeball metric'' cannot be used for machine learning where an algorithmic description is required. Motivated by the success of intermediate neural network activations as basis for learned metrics, e.g. in computer vision, we present a novel, self-supervised representation learning approach specifically designed for atmospheric dynamics. Our approach, called AtmoDist, trains a neural network on a simple, auxiliary task: predicting the temporal distance between elements of a randomly shuffled sequence of atmospheric fields (e.g. the components of the wind field from reanalysis or simulation). The task forces the network to learn important intrinsic aspects of the data as activations in its layers and from these hence a discriminative metric can be obtained. We demonstrate this by using AtmoDist to define a metric for GAN-based super resolution of vorticity and divergence. Our upscaled data matches both visually and in terms of its statistics a high resolution reference closely and it significantly outperform the state-of-the-art based on mean squared error. Since AtmoDist is unsupervised, only requires a temporal sequence of fields, and uses a simple auxiliary task, it has the potential to be of utility in a wide range of applications.

Authors: Sebastian Hoffmann (University of Magdeburg); Christian Lessig (Otto-von-Guericke-Universitat Magdeburg)

Climate Science & Modeling Earth Observation & Monitoring Generative Modeling Time-series Analysis
NeurIPS 2021 Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion (Papers Track)
Abstract and authors: (click to expand)

Abstract: Global warming leads to the increase in frequency and intensity of climate extremes that cause tremendous loss of lives and property. Accurate long-range climate prediction allows more time for preparation and disaster risk management for such extreme events. Although machine learning approaches have shown promising results in long-range climate forecasting, the associated model uncertainties may reduce their reliability. To address this issue, we propose a late fusion approach that systematically combines the predictions from multiple models to reduce the expected errors of the fused results. We also propose a network architecture with the novel denormalization layer to gain the benefits of data normalization without actually normalizing the data. The experimental results on long-range 2m temperature forecasting show that the framework outperforms the 30-year climate normals, and the accuracy can be improved by increasing the number of models.

Authors: Ken C. L. Wong (IBM Research – Almaden Research Center); Hongzhi Wang (IBM Almaden Research Center); Etienne E Vos (IBM); Bianca Zadrozny (IBM Research); Campbell D Watson (IBM Reserch); Tanveer Syeda-Mahmood (IBM Research)

Uncertainty Quantification & Robustness Climate Science & Modeling Time-series Analysis
NeurIPS 2021 Predicting Discharge in Catchment Outlet Using Deep Learning: Case Study of the Ansongo-Niamey Basin (Papers Track)
Abstract and authors: (click to expand)

Abstract: Hydrological models are one of the key challenges in hydrology. Their goal is to understand, predict and manage water resources. Most of the hydrological models so far were either physical or conceptual models. But in the past two decades, fully data-driven (empirical) models started to emerge with the breakthroughs of novel deep learning methods in runoff prediction. These breakthroughs were mostly favored by the large volume, variety and velocity of water-related data. Long Short-Term Memory and Gated Recurrent Unit neural networks, particularly achieved the outstanding milestone of outperforming classic hydrological models in less than a decade. Moreover, they have the potential to change the way hydrological modeling is performed. In this study, precipitation, minimal and maximum temperature at the Ansongo-Niamey basin combined with the discharge at Ansongo and Kandadji were used to predict the discharge at Niamey using artificial neural networks. After data preprocessing and hyperparameter optimization, the deep learning models performed well with the LSTM and GRU respectively scoring a Nash-Sutcliffe Efficiency of 0.933 and 0.935. This performance matches those of well-known physically-based models used to simulate Niamey’s discharge and therefore demonstrates the efficiency of deep learning methods in a West African context, especially in Niamey which has been facing severe floods due to climate change.

Authors: Peniel J. Y. Adounkpe (WASCAL); Eric Alamou (Université d'Abomey-Calavi); Belko Diallo (WASCAL); Abdou Ali (AGRHYMET Regional Centre)

Disaster Management and Relief Causal & Bayesian Methods Time-series Analysis
NeurIPS 2021 Accurate and Timely Forecasts of Geologic Carbon Storage using Machine Learning Methods (Papers Track)
Abstract and authors: (click to expand)

Abstract: Carbon capture and storage is one strategy to reduce greenhouse gas emissions. One approach to storing the captured CO2 is to inject it into deep saline aquifers. However, dynamics of the injected CO2 plume is uncertain and the potential for leakage back to the atmosphere must be assessed. Thus, accurate and timely forecasts of CO2 storage via real-time measurements integration becomes very crucial. This study proposes a learning-based, inverse-free prediction method that can accurately and rapidly forecast CO2 movement and distribution with uncertainty quantification based on limited simulation and observation data. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO2 storage site operators with an effective tool for real-time decision making.

Authors: Dan Lu (Oak Ridge National Laboratory); Scott Painter (Oak Ridge National Laboratory); Nicholas Azzolina (University of North Dakota); Matthew Burton-Kelly (University of North Dakota)

Carbon Capture & Sequestration Uncertainty Quantification & Robustness
NeurIPS 2021 Towards debiasing climate simulations using unsuperviserd image-to-image translation networks (Papers Track)
Abstract and authors: (click to expand)

Abstract: Climate models form the basis of a vast portion of earth system research, and inform our climate policy. Due to the complex nature of our climate system, and the approximations which must necessarily be made in simulating it, these climate models may not perfectly match observations. For further research, these outputs must be bias corrected against observations, but current methods of debiasing do not take into account spatial correlations. We evaluate unsupervised image-to-image translation networks, specifically the UNIT model architecture, for their ability to produce more spatially realistic debiasing than the standard techniques used in the climate community.

Authors: James Fulton (University of Edinburgh); Ben Clarke (Oxford University)

Climate Science & Modeling Generative Modeling
NeurIPS 2021 Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific (Papers Track)
Abstract and authors: (click to expand)

Abstract: Aerosol-cloud interactions include a myriad of effects that all begin when aerosol enters a cloud and acts as cloud condensation nuclei (CCN). An increase in CCN results in a decrease in the mean cloud droplet size (r$_{e}$). The smaller droplet size leads to brighter, more expansive, and longer lasting clouds that reflect more incoming sunlight, thus cooling the earth. Globally, aerosol-cloud interactions cool the Earth, however the strength of the effect is heterogeneous over different meteorological regimes. Understanding how aerosol-cloud interactions evolve as a function of the local environment can help us better understand sources of error in our Earth system models, which currently fail to reproduce the observed relationships. In this work we use recent non-linear, causal machine learning methods to study the heterogeneous effects of aerosols on cloud droplet radius.

Authors: Andrew Jesson (University of Oxford); Peter Manshausen (University of Oxford); Alyson Douglas (University of Oxford); Duncan Watson-Parris (University of Oxford); Yarin Gal (University of Oxford); Philip Stier (University of Oxford)

Climate Science & Modeling Causal & Bayesian Methods
NeurIPS 2021 Memory to Map: Improving Radar Flood Maps With Temporal Context and Semantic Segmentation (Papers Track)
Abstract and authors: (click to expand)

Abstract: Global flood risk has increased due to worsening extreme weather events and human migration into growing flood-prone areas. Accurate, high-resolution, and near-real time flood maps can address flood risk by reducing financial loss and damage. We propose Model to Map, a novel machine learning approach that utilizes bi-temporal context to improve flood water segmentation performance for Sentinel-1 imagery. We show that the inclusion of unflooded context for the area, or "memory," allows the model to tap into a "prior state" of pre-flood conditions, increasing performance in geographic regions in which single-image radar-based flood mapping methods typically underperform (e.g. deserts). We focus on accuracy across different biomes to ensure global performance. Our experiments and novel data processing technique show that the confluence of pre-flood and permanent water context provides a 21% increase in mIoU over the baseline overall, and over 87% increase in deserts.

Authors: Veda Sunkara (Cloud to Street); Nicholas Leach (Cloud to Street); Siddha Ganju (Nvidia)

Computer Vision & Remote Sensing Climate Justice Earth Observation & Monitoring
NeurIPS 2021 Hurricane Forecasting: A Novel Multimodal Machine Learning Framework (Papers Track)
Abstract and authors: (click to expand)

Abstract: This paper describes a machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple distinct ML techniques and utilizing diverse data sources. Our framework, which we refer to as Hurricast (HURR), is built upon the combination of distinct data processing techniques using gradient-boosted trees and novel encoder-decoder architectures, including CNN, GRU and Transformers components. We propose a deep-learning feature extractor methodology to mix spatial-temporal data with statistical data efficiently. Our multimodal framework unleashes the potential of making forecasts based on a wide range of data sources, including historical storm data, and visual data such as reanalysis atmospheric images. We evaluate our models with current operational forecasts in North Atlantic (NA) and Eastern Pacific (EP) basins on 2016-2019 for 24-hour lead time, and show our models consistently outperform statistical-dynamical models and compete with the best dynamical models. Furthermore, the inclusion of Hurricast into an operational forecast consensus model leads to a significant improvement of 5% - 15% over NHC's official forecast, thus highlighting the complementary properties with existing approaches.

Authors: Léonard Boussioux (MIT, CentraleSupélec); Cynthia Zeng (MIT); Dimitris Bertsimas (MIT); Théo J Guenais (Harvard University)

Hybrid Physical Models Climate Science & Modeling Disaster Management and Relief Computer Vision & Remote Sensing Time-series Analysis Unsupervised & Semi-Supervised Learning
NeurIPS 2021 Improved Drought Forecasting Using Surrogate Quantile And Shape (SQUASH) Loss (Papers Track)
Abstract and authors: (click to expand)

Abstract: Droughts are amongst the most damaging natural hazard with cascading impacts across multiple sectors of the economy and society. Improved forecasting of drought conditions ahead of time can significantly improve strategic planning to mitigate the impacts and enhance resilience. Though significant progress in forecasting approaches has been made, the current approaches focus on the overall improvement of the forecast, with less attention on the extremeness of drought events. In this paper, we focus on improving the accuracy of forecasting extreme and severe drought events by introducing a novel loss function Surrogate Quantile and Shape loss (SQUASH) that combines weighted quantile loss and dynamic time-warping-based shape loss. We show the effectiveness of the proposed loss functions for imbalanced time-series drought forecasting tasks on two regions in India and the USA.

Authors: Devyani Lambhate Lambhate (Indian Institute of Science); Smit Marvaniya (IBM Research India); Jitendra Singh (IBM Research - India); David Gold (IBM)

Disaster Management and Relief Climate Science & Modeling Time-series Analysis
NeurIPS 2021 Global ocean wind speed estimation with CyGNSSnet (Papers Track)
Abstract and authors: (click to expand)

Abstract: The CyGNSS (Cyclone Global Navigation Satellite System) satellite system measures GNSS signals reflected off the Earth's surface. A global ocean wind speed dataset is derived, which fills a gap in Earth observation data, will improve cyclone forecasting, and could be used to mitigate effects of climate change. We propose CyGNSSnet, a deep learning model for predicting wind speed from CyGNSS observables, and evaluate its potential for operational use. With CyGNSSnet, performance improves by 29\% over the current operational model. We further introduce a hierarchical model, that combines an extreme value classifier and a specialized CyGNSSnet and slightly improves predictions for high winds.

Authors: Caroline Arnold (German Climate Computing Center); Milad Asgarimehr (German Research Centre for Geosciences)

Computer Vision & Remote Sensing Disaster Management and Relief Earth Observation & Monitoring
NeurIPS 2021 Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring (Papers Track)
Abstract and authors: (click to expand)

Abstract: The Biogeochemical-Argo (BGC-Argo) program is building a network of globally distributed, sensor-equipped robotic profiling floats, improving our understanding of the climate system and how it is changing. These floats, however, are limited in the number of variables measured. In this study, we train neural networks to predict silicate and phosphate values in the Southern Ocean from temperature, pressure, salinity, oxygen, nitrate, and location and apply these models to earth system model (ESM) and BGC-Argo data to expand the utility of this ocean observation network. We trained our neural networks on observations from the Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP) and use dropout regularization to provide uncertainty bounds around our predicted values. Our neural network significantly improves upon linear regression but shows variable levels of uncertainty across the ranges of predicted variables. We explore the generalization of our estimators to test data outside our training distribution from both ESM and BGC-Argo data. Our use of out-of-distribution test data to examine shifts in biogeochemical parameters and calculate uncertainty bounds around estimates advance the state-of-the-art in oceanographic data and climate monitoring. We make our data and code publicly available.

Authors: Ellen Park (MIT); Jae Deok Kim (MIT-WHOI); Nadege Aoki (MIT); Yumeng Cao (MIT); Yamin Arefeen (Massachusetts Institute of Technology); Matthew Beveridge (Massachusetts Institute of Technology); David P Nicholson (Woods Hole Oceanographic Institution); Iddo Drori (MIT)

Earth Observation & Monitoring Ecosystems & Biodiversity
NeurIPS 2021 On the Generalization of Agricultural Drought Classification from Climate Data (Papers Track)
Abstract and authors: (click to expand)

Abstract: Climate change is expected to increase the likelihood of drought events, with severe implications for food security. Unlike other natural disasters, droughts have a slow onset and depend on various external factors, making drought detection in climate data difficult. In contrast to existing works that rely on simple relative drought indices as ground-truth data, we build upon soil moisture index (SMI) obtained from a hydrological model. This index is directly related to insufficiently available water to vegetation. Given ERA5-Land climate input data of six months with land use information from MODIS satellite observation, we compare different models with and without sequential inductive bias in classifying droughts based on SMI. We use PR-AUC as the evaluation measure to account for the class imbalance and obtain promising results despite a challenging time-based split. We further show in an ablation study that the models retain their predictive capabilities given input data of coarser resolutions, as frequently encountered in climate models.

Authors: Julia Gottfriedsen (1Deutsches Zentrum für Luft- und Raumfahrt (DLR), LMU); Max Berrendorf (Ludwig-Maximilians-Universität München); Pierre Gentine (Columbia University); Markus Reichstein (Max Planck Institute for Biogeochemistry, Jena; Michael Stifel Center Jena for Data-Driven and Simulation Science, Jena); Katja Weigel (niversity of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany); Birgit Hassler (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany); Veronika Eyring (Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany; University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany)

Climate Science & Modeling Disaster Management and Relief Agriculture & Food
NeurIPS 2021 Mapping Post-Climate Change Biogeographical Regions with Deep Latent Variable Models (Papers Track)
Abstract and authors: (click to expand)

Abstract: Forecasting future changes to biodiversity due to shifts in climate is challenging due to nonlinear interactions between species as recorded in their presence/absence data. This work proposes using variational autoencoders with environmental covariates to identify low-dimensional structure in species’ joint co-occurrence patterns and leveraging this simplified representation to provide multivariate predictions of their habitat extent under future climate scenarios. We pursue a latent space clustering approach to map biogeographical regions of frequently co-occurring species and apply this methodology to a dataset from northern Belgium, generating predictive maps illustrating how these regions may expand or contract with changing temperature under a future climate scenario.

Authors: Christopher Krapu (Oak Ridge National Lab - Oak Ridge, TN)

Generative Modeling Causal & Bayesian Methods Land Use
NeurIPS 2021 Rotation Equivariant Deforestation Segmentation and Driver Classification (Papers Track)
Abstract and authors: (click to expand)

Abstract: Deforestation has become a significant contributing factor to climate change and, due to this, both classifying the drivers and predicting segmentation maps of deforestation has attracted significant interest. In this work, we develop a rotation equivariant convolutional neural network model to predict the drivers and generate segmentation maps of deforestation events from Landsat 8 satellite images. This outperforms previous methods in classifying the drivers and predicting the segmentation map of deforestation, offering a 9% improvement in classification accuracy and a 7% improvement in segmentation map accuracy. In addition, this method predicts stable segmentation maps under rotation of the input image, which ensures that predicted regions of deforestation are not dependent upon the rotational orientation of the satellite.

Authors: Joshua Mitton (University of Glasgow); Roderick Murray-Smith (University of Glasgow)

Computer Vision & Remote Sensing
NeurIPS 2021 WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data (Papers Track)
Abstract and authors: (click to expand)

Abstract: The transition to green energy grids depends on detailed wind and solar forecasts to optimize the siting and scheduling of renewable energy generation. Operational forecasts from numerical weather prediction models, however, only have a spatial resolution of 10 to 20-km, which leads to sub-optimal usage and development of renewable energy farms. Weather scientists have been developing super-resolution methods to increase the resolution, but often rely on simple interpolation techniques or computationally expensive differential equation-based models. Recently, machine learning-based models, specifically the physics-informed resolution-enhancing generative adversarial network (PhIREGAN), have outperformed traditional downscaling methods. We provide a thorough and extensible benchmark of leading deep learning-based super-resolution techniques, including the enhanced super-resolution generative adversarial network (ESRGAN) and an enhanced deep super-resolution (EDSR) network, on wind and solar data. We accompany the benchmark with a novel public, processed, and machine learning-ready dataset for benchmarking super-resolution methods on wind and solar data.

Authors: Rupa Kurinchi-Vendhan (Caltech); Björn Lütjens (MIT); Ritwik Gupta (University of California, Berkeley); Lucien D Werner (California Institute of Technology); Dava Newman (MIT); Steven Low (California Institute of Technology)

Computer Vision & Remote Sensing Climate Science & Modeling Earth Observation & Monitoring Power & Energy Generative Modeling
NeurIPS 2021 Meta-Learned Bayesian Optimization for Calibrating Building Simulation Models with Multi-Source Data (Papers Track)
Abstract and authors: (click to expand)

Abstract: Well-calibrated building simulation models are key to reducing greenhouse gas emissions and optimizing building performance. Current calibration algorithms do not leverage data collected during previous calibration tasks. In this paper, we employ attentive neural processes (ANP) to meta-learn a distribution using multi-source data acquired during previously seen calibration tasks. The ANP informs a meta-learned Bayesian optimizer to accelerate calibration of new, unseen tasks. The few-shot nature of our proposed algorithm is demonstrated on a library of residential buildings validated by the United States Department of Energy (USDoE).

Authors: Sicheng Zhan (NUS); Gordon Wichern (Mitsubishi Electric Research Laboratories (MERL)); Christopher Laughman (Mitsubishi Electric Research Laboratories); Ankush Chakrabarty (Mitsubishi Electric Research Labs)

Buildings Meta- and Transfer Learning
NeurIPS 2021 MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather (Papers Track)
Abstract and authors: (click to expand)

Abstract: We present the encoder-forecaster convolutional long short-term memory (LSTM) deep-learning model that powers Microsoft Weather's operational precipitation nowcasting product. This model takes as input a sequence of weather radar mosaics and deterministically predicts future radar reflectivity at lead times up to 6 hours. By stacking a large input receptive field along the feature dimension and conditioning the model's forecaster with predictions from the physics-based High Resolution Rapid Refresh (HRRR) model, we are able to outperform optical flow and HRRR baselines by 20-25% on multiple metrics averaged over all lead times.

Authors: Sylwester Klocek (Microsoft Corporation); Haiyu Dong (Microsoft); Matthew Dixon (Microsoft Corporation); Panashe Kanengoni (Microsoft Corporation); Najeeb Kazmi (Microsoft); Pete Luferenko (Microsoft Corporation); Zhongjian Lv (Microsoft Corporation); Shikhar Sharma (); Jonathan Weyn (Microsoft); Siqi Xiang (Microsoft Corporation)