2025/10/10 by Yuhang Wang, Shi, Hongzheng, Xiao Liu +2
Earth and Planetary Sciences · Engineering · Environmental Science · #Applications (stat.AP) #Computational Engineering #FOS: Computer and information sciences #Finance #Fire Detection and Safety Systems #Fire effects on ecosystems #Machine Learning (cs.LG) #Meteorological Phenomena and Simulations #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2510.15954
openalex publication_date 2025/10/10 · openalex created_date 2025/10/22 · openalex updated_date 2026/07/28
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.