Fire-EnSF: Wildfire Spread Data Assimilation using Ensemble Score Filter
This work addresses the need for accurate wildfire management by improving forecasting, though it is incremental as it adapts an existing method to a specific domain.
The paper tackled real-time wildfire spread prediction by applying the Ensemble Score Filter (EnSF), a diffusion-model-based algorithm, to data assimilation, demonstrating superior accuracy, stability, and computational efficiency in numerical tests.
As wildfires become increasingly destructive and expensive to control, effective management of active wildfires requires accurate, real-time fire spread predictions. To enhance the forecasting accuracy of active fires, data assimilation plays a vital role by integrating observations (such as remote-sensing data) and fire predictions generated from numerical models. This paper provides a comprehensive investigation on the application of a recently proposed diffusion-model-based filtering algorithm -- the Ensemble Score Filter (EnSF) -- to the data assimilation problem for real-time active wildfire spread predictions. Leveraging a score-based generative diffusion model, EnSF has been shown to have superior accuracy for high-dimensional nonlinear filtering problems, making it an ideal candidate for the filtering problems of wildfire spread models. Technical details are provided, and our numerical investigations demonstrate that EnSF provides superior accuracy, stability, and computational efficiency, establishing it as a robust and practical method for wildfire data assimilation. Our code has been made publicly available.