Tuned Forest Fire Prediction: Static Calibration of the Evolutionary Component of ESS

Forest fires are a major risk factor with strong impact at eco-environmental and socioeconomical levels, reasons why their study and modeling are very important. However, the models frequently have a certain level of uncertainty in some input parameters given that they must be approximated or estimated, as a consequence of diverse difficulties to accurately measure the conditions of the phenomenon in real time. This has resulted in the development of several methods for the uncertainty reduction, whose trade-off between accuracy and complexity can vary significantly. The system ESS (EvolutionaryStatistical System) is a method whose aim is to reduce the uncertainty, by combining Statistical Analysis, High Performance Computing (HPC) and Parallel Evolutionary Algorithms (PEAs). The PEAs use several parameters that require adjustment and that determine the quality of their use. The calibration of the parameters is a crucial task for reaching a good performance and to improve the system output. This paper presents an empirical study of the parameters tuning to evaluate the effectiveness of different configurations and the impact of their use in the Forest Fires prediction.