Refining a Parameter Setting Evolutionary Approach for Fire Spreading Models Based on Cellular Automata

Forest fires have increased significantly due to climate change affecting diverse biomes. Fire propagation modeling is essential in preventing and controlling the damage caused by this phenomenon. Cellular automata were demonstrated to be effective when constructing such models. However, adjusting the many parameters involved in these models is a complex task. Recently, an evolutionary approach to parameter adjustments of a fire simulation model based on CA has been proposed. This paper aims to continue this study by refining the method. Different experiments were carried out to analyze the sensitivity of the evolutionary approach to parameter adjustment, including the generation of bases from other models and the inclusion of heterogeneous vegetation.