Short-term forecasting of wildfire spread: A network epidemiology approach

2026-09-13

AI Summary

本文提出结合元胞自动机与网络流行病学的方法，用于短期预测野火蔓延，通过气象和环境协变量影响状态转换率，实现对野火演化的建模。

Abstract

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.

Problem

• wildfire spread
• short-term forecasting
• environmental risks
• public-health risks

Innovation

• cellular automata
• network epidemiology
• short-term forecasting
• wildfire spread
• meteorological and environmental covariates