Random Forest-Informed Cellular Automaton for Large-Scale Wildfire Spread Modelling

Abstract:Accurate large-scale wildfire spread modelling requires models that capture both the environmental conditions associated with fire occurrence and the local dynamics of fire propagation. We propose a three-stage framework that combines a Random Forest (RF) model with a cellular automaton (CA). First, an RF model trained on the 2021 Canadian fire season estimates daily pixel-level fire-occurrence probabilities. Second, quantile gradient boosting models provide optional spread-rate priors for sensitivity analysis. Third, an RF-informed CA combines the RF probability layer with neighbourhood-driven spread on a 5 km grid. The RF model achieved AUC values of 0.725--0.795 on the 2022--2024 datasets, while the RF-informed CA achieved substantially higher spatial overlap than the evaluated CA-only baselines in the 2023 simulation. A higher-resolution simulation provides an additional qualitative assessment of local spatial errors. These results suggest that combining RF-derived probabilities with local CA spread can improve large-scale wildfire simulations under the tested conditions.

Applications (stat.AP); Machine Learning (cs.LG)

arXiv:2609.01675 [stat.AP]

(or arXiv:2609.01675v1 [stat.AP] for this version)

https://doi.org/10.48550/arXiv.2609.01675 Focus to learn more arXiv-issued DOI via DataCite

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