A comparative study of evolutionary statistical methods for uncertainty reduction in forest fire propagation prediction

Abstract

Predicting the propagation of forest fires is a crucial point to mitigate their effects. Therefore, several computational tools or simulators have been developed to predict the fire propagation. Such tools consider the scenario (topography, vegetation types, fire front situation), and the particular conditions where the fire is evolving (vegetation conditions, meteorological conditions) to predict the fire propagation. However, these parameters are usually difficult to measure or estimate precisely, and there is a high degree of uncertainty in many of them. This uncertainty provokes a certain lack of accuracy in the predictions with the consequent risks. So, it is necessary to apply methods to reduce the uncertainty in the input parameters. This work presents a comparison of ESSIM-EA and ESSIM-DE: two methods to reduce the uncertainty in the input parameters. These methods combine Evolutionary Algorithms, Parallelism and Statistical Analysis to improve the propagation prediction.