Random Forest-Informed Cellular Automaton for Large-Scale Wildfire Spread Modelling
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 datas...
| Authors | Siyu Chen, Esha Saha, Hao Wang |
|---|---|
| Primary category | stat.AP |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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