Automatic Evolutionary Adjustment of Cellular Automata Model for Forest Fire Propagation.

Forest fires have faced a huge increase due to climate change threatening different biomes. Fire propagation modeling is a critical issue to prevent and control the damage of this phenomenon. Cellular automata shown to be efficient in the construction of such models. However, tuning the various parameters involved in these models is a difficult task. This work proposes a method of parameters adjustment of fire propagation models based on genetic algorithms. Different experiments in various scenarios showed that was possible to use evolutionary computation that automatically adjusts the parameters of a fire spread model.