结合随机森林的元胞自动机用于大规模野火蔓延建模
Random Forest-Informed Cellular Automaton for Large-Scale Wildfire Spread Modelling
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中文总结 AI 辅助
该研究提出结合随机森林与元胞自动机的三阶段框架，用于大规模野火蔓延建模，在2022-2024年数据集上取得了0.725-0.795的AUC值，模拟效果优于仅用元胞自动机的基线模型。
中文摘要 AI 辅助
准确的大规模野火蔓延建模需要同时捕捉与火灾发生相关的环境条件和火灾蔓延的局部动态。我们提出了一个三阶段框架，将随机森林（Random Forest, RF）模型与元胞自动机（Cellular Automaton, CA）相结合。首先，在2021年加拿大火灾季数据上训练的RF模型，可估算每日像素级的火灾发生概率。其次，分位数梯度提升模型为敏感性分析提供可选的蔓延率先验。第三，结合RF概率层与邻域驱动蔓延的RF元胞自动机（RF-informed CA），在5公里网格上运行。RF模型在2022-2024年数据集上的AUC值为0.725至0.795，而RF元胞自动机在2023年模拟中，相比仅使用CA的基线模型实现了显著更高的空间重叠度。更高分辨率的模拟则对局部空间误差提供了额外的定性评估。这些结果表明，在测试条件下，将RF衍生的概率与局部CA蔓延相结合，可改进大规模野火模拟效果。
英文摘要
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.
