Fire Spread Modeling Using Probabilistic Cellular Automata

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. This methodology is applied to case studies of the 1990 wildfire on Spetses Island, Greece, offering insights into the effects of terrain on fire spread, as well as the 2021 Evia Island wildfire in Greece, demonstrating the model’s accuracy in simulating real-world wildfire scenarios.