发表机构
Louisiana State University; North Carolina State University; University of Illinois at Urbana–Champaign（路易斯安那州立大学; 北卡罗来纳州立大学; 伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种结合元胞自动机与网络流行病学的统计框架，用于野火短期蔓延预测，在模拟和真实野火数据中展现出较强短期判别能力，并为点火预测改进提供了方向。
AI 中文摘要
野火蔓延对环境和公共健康构成重大风险，这促使人们开发可解释的模型用于短期预测。我们开发了一个统计框架，将元胞自动机与网络流行病学的思想相结合，以模拟野火在空间网格上的演化过程。每个网格单元被分类为可用、燃烧或已燃尽。状态转换区分了来自燃烧邻居的蔓延、自然点火以及燃烧的停止，转换率与气象和环境协变量相关联。基于似然的估计程序可得出特定转换的协变量效应以及单元状态的概率预测。我们在模拟研究以及2018年加利福尼亚野火和2019-2020年澳大利亚野火的应用中评估了预测性能。我们还将该方法与使用2017年Haypress火灾的已发表预测方法进行了比较。结果表明，在许多情况下短期判别能力较强，但在较长的预测时间范围以及野火突然扩张期间，准确性有所下降。该框架为研究野火动态提供了可解释的基础，并指出了通过更丰富的空间和观测模型改进点火预测的机会。
英文摘要
Wildfire spread poses substantial environmental and public-health risks, motivating interpretable models for short-term forecasting. We develop a statistical framework that combines cellular automata with ideas from network epidemiology to model wildfire evolution across a spatial lattice. Each grid cell is classified as available, burning, or consumed. State transitions distinguish spread from burning neighbors, intrinsic ignition, and cessation of burning, with transition rates linked to meteorological and environmental covariates. A likelihood-based estimation procedure yields transition-specific covariate effects and probabilistic forecasts of cell states. We assess forecasting performance in a simulation study and in applications to the 2018 California wildfires and the 2019-2020 Australian wildfires. We also compare the method with a published forecasting approach using the 2017 Haypress fire. The results show strong short-term discrimination in many settings, with reduced accuracy at longer forecast horizons and during abrupt fire expansion. The framework provides an interpretable basis for studying wildfire dynamics and identifies opportunities to improve ignition forecasts through richer spatial and observation models.
