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Workshop: NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems

Fusion of Physics-Based Wildfire Spread Models with Satellite Data using Generative Algorithms

Bryan Shaddy · Deep Ray · Angel Farguell · Valentina Calaza · Jan Mandel · James Haley · Kyle Hilburn · Derek Mallia · Adam Kochanski · Assad Oberai


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.

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