Evolutionary Adjustment of a Cellular Automata-BasedModel for Wildfire Spreading

• Lucas V. Murilo UFU
• Gina M. B. Oliveira UFU
• Luiz G. A. Martins UFU

Resumo

Due to climate change, the incidence of fires has increased considerably in recent decades, causing various damages to the environment. In this scenario, computational models based on Cellular Automata (CA) are capable of reproducing and predicting the behavior of fire propagation, aiding in control and prevention measures. However, constructing and adjusting the parameters of these models is not a trivial task. In this context, a method capable of automatically adjusting these parameters can assist in this stage, which demands deep knowledge on the subject. In this work a Genetic Algorithm (GA) is presented aiming the adequate adjusting of a CA-based model parameter set. Additionally, a new parameter is proposed allowing the adaptability of the CA model to the fire spreading dynamics, regardless of the sampling rate used to generate the reference data. The experiments shown that our approach was able to capture the dynamics present in the data.