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New wildfire model combines Random Forest and Cellular Automaton

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel three-stage framework for large-scale wildfire spread modeling that integrates a Random Forest (RF) model with a cellular automaton (CA). The framework first uses an RF model trained on the 2021 Canadian fire season to estimate daily pixel-level fire-occurrence probabilities. This is followed by quantile gradient boosting models for optional spread-rate priors and an RF-informed CA that combines the RF probability layer with neighborhood-driven spread on a 5 km grid. The RF model demonstrated strong performance with AUC values between 0.725 and 0.795 on subsequent datasets, and the combined RF-informed CA showed significantly improved spatial overlap compared to CA-only baselines in 2023 simulations. AI

IMPACT This new modeling framework could improve the accuracy of large-scale wildfire simulations by integrating machine learning probabilities with local spread dynamics.

RANK_REASON The cluster contains an academic paper detailing a new modeling approach. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New wildfire model combines Random Forest and Cellular Automaton

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The cluster contains an academic paper detailing a new modeling approach. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Siyu Chen, Esha Saha, Hao Wang ·

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

    arXiv:2609.01675v1 Announce Type: cross 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 comb…

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