Fire Spread Modeling using Probabilistic Cellular Automata

TL;DR

This paper presents a probabilistic cellular automaton model for wildfire spread that integrates GIS data and key environmental factors to improve prediction accuracy of complex fire dynamics.

Contribution

It introduces an enhanced CA-based wildfire model incorporating probabilistic transitions, wind, vegetation, and spotting effects, advancing existing modeling approaches.

Findings

Effective simulation of wildfire spread dynamics

Incorporation of environmental factors improves prediction accuracy

Model captures complex fire behaviors like spotting

Abstract

A cellular automaton (CA)-based modeling approach to simulate wildfire spread, emphasizing its strengths in capturing complex fire dynamics and its integration with geographic information systems (GIS). The model introduces an enhanced CA-based methodology for wildfire prediction, emphasizing interactions between neighboring cells and incorporating major determinants of fire spread, including wind direction, wind speed, and vegetation density, while also accounting for spotting and probabilistic transitions between states in the model to mirror real-world fire behavior.