Wildfires are becoming increasingly frequent and devastating, and therefore the technology to combat them must adapt accordingly. Modern predictive models have failed to balance predictive accuracy and operational viability, resulting in consistently delayed or misinformed fire suppression and public safety efforts. The present study addresses this gap by developing and validating a predictive model based on cellular automata (CA) that incorporates key environmental variables, including vegetation density (NDVI), wind speed and direction, and topographic slope derived from open-access datasets. The presented CA framework offers a lightweight alternative to data-heavy approaches that fail in emergency contexts. Evaluation of the model using a confusion matrix against burn scars from the 2025 Pacific Palisades Fire yielded a recall of 0.860, a precision of 0.605, and an overall F1 score of 0.711 after 50 parameter optimization trials, with each simulation taking an average of 1.22 seconds. CA-based models can bridge the gap between accuracy and applicability, successfully guiding public safety and fire suppression efforts.

翻译：野火正变得日益频繁且破坏性加剧，因此应对野火的技术必须相应发展。现代预测模型未能平衡预测精度与操作可行性，导致灭火与公共安全工作持续面临延误或信息误判。本研究通过开发并验证一种基于元胞自动机（CA）的预测模型来弥补这一不足，该模型整合了关键环境变量，包括植被密度（NDVI）、风速与风向，以及从开放数据集中提取的地形坡度。所提出的CA框架为数据密集型方法提供了一种轻量级替代方案，后者在紧急情境下往往失效。通过使用混淆矩阵对照2025年太平洋帕利塞德火灾的过火痕迹对模型进行评估，经过50次参数优化试验后，召回率达到0.860，精确度为0.605，整体F1分数为0.711，每次模拟平均耗时1.22秒。基于CA的模型能够弥合准确性与适用性之间的差距，有效指导公共安全与灭火工作。