Predicting Wildfire Risk Under Novel 21st-Century Climate Conditions

Matthew Cooper (Sust Global).

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Disaster Management and Relief Interpretable ML Uncertainty Quantification & Robustness


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.