GraphTransformers for Geospatial Forecasting of Hurricane Trajectories (Papers Track)

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

Paper PDF Poster File NeurIPS 2023 Poster Cite
Disaster Management and Relief Climate Science & Modeling


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.