[2603.26975] Probabilistic Forecasting of Localized Wildfire Spread Based on Conditional Flow Matching

Computer Science > Machine Learning arXiv:2603.26975 (cs) [Submitted on 27 Mar 2026] Title:Probabilistic Forecasting of Localized Wildfire Spread Based on Conditional Flow Matching Authors:Bryan Shaddy, Haitong Qin, Brianna Binder, James Haley, Riya Duddalwar, Kyle Hilburn, Assad Oberai View a PDF of the paper titled Probabilistic Forecasting of Localized Wildfire Spread Based on Conditional Flow Matching, by Bryan Shaddy and 6 other authors View PDF HTML (experimental) Abstract:This study presents a probabilistic surrogate model for localized wildfire spread based on a conditional flow matching algorithm. The approach models fire progression as a stochastic process by learning the conditional distribution of fire arrival times given the current fire state along with environmental and atmospheric inputs. Model inputs include current burned area, near-surface wind components, temperature, relative humidity, terrain height, and fuel category information, all defined on a high-resolution spatial grid. The outputs are samples of arrival time within a three-hour time window, conditioned on the input variables. Training data are generated from coupled atmosphere-wildfire spread simulations using WRF-SFIRE, paired with weather fields from the North American Mesoscale model. The proposed framework enables efficient generation of ensembles of arrival times and explicitly represents uncertainty arising from incomplete knowledge of the fire-atmosphere system and unresolved variables....