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FireGraphNet advances wildfire modeling by leveraging directed graph neural networks to predict 12-hour spread dynamics with significantly higher precision than daily-scale baselines. The study isolates message passing mechanisms as the primary driver of this skill, demonstrating that architectural choices like candidate construction and edge aggregation critically shape model performance in operational settings. This work offers a robust framework for near-real-time fire behavior analysis, directly addressing the temporal resolution gap in current learned spread models.
FireGraphNet: Sub-daily wildfire spread prediction with directed graph neural networks
arXiv:2610.19191v1 Announce Type: new Abstract: Wildfire decisions are made in hours, yet most learned spread models predict daily on targets including burned area. We propose FireGraphNet which formulates 12-hour growth as node classification on directed H3 graphs, with candidates ahead of the front and wind alignment, terrain, fuel continuity and barriers on edges. On satellite perimeters it achieves average precision 1.7-1.9x the strongest distance baseline on test California fires and 1.62x out of state. We show this skill comes from message passing rather than cell features or the aggregator, is highest on young fires in state, is carried deep along the wind by its edges, and is hidden by pooling. A convolutional network matches this ranking once trained on new growth, at worse calibration, so candidate construction, target definition, and aggregation shape conclusions about model skill. Editing fuel and barrier attributes reduces predicted spread more under guided than random placement, extending FireGraphNet to operational intervention analysis.
Abstract:Wildfire decisions are made in hours, yet most learned spread models predict daily on targets including burned area. We propose FireGraphNet which formulates 12-hour growth as node classification on directed H3 graphs, with candidates ahead of the front and wind alignment, terrain, fuel continuity and barriers on edges. On satellite perimeters it achieves average precision 1.7-1.9x the strongest distance baseline on test California fires and 1.62x out of state. We show this skill comes from message passing rather than cell features or the aggregator, is highest on young fires in state, is carried deep along the wind by its edges, and is hidden by pooling. A convolutional network matches this ranking once trained on new growth, at worse calibration, so candidate construction, target definition, and aggregation shape conclusions about model skill. Editing fuel and barrier attributes reduces predicted spread more under guided than random placement, extending FireGraphNet to operational intervention analysis.
Machine Learning (cs.LG)
arXiv:2610.19191 [cs.LG]
(or arXiv:2610.19191v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.19191 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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