Wildfire Spread Prediction Model Calibration Using Metaheuristic Algorithms

Abstract

Every year, wildfires cause significant losses and destruction around the globe. In order to attempt to reduce their damages, resources have been put into developing fire propagation prediction systems. In a real wildfire event, these systems provide the authorities with information about the fire propagation in the near future, thus allowing them to make better decisions. Wildfire spread prediction systems are based on fire propagation models, from which the most used and accepted model is the Rothermel model. However, given the complexity of the wildfire phenomena and the uncertainty of some of its input parameter values, the Rothermel model can produce misleading results of fire propagation. This paper uses 3 metaheuristic algorithms, genetic algorithm (GA), differential evolution (DE) and simulated annealing (SA), for calibration of input parameters from the Rothermel model. These algorithms were validated using 37 datasets containing data from controlled experimental fires. Results have shown that these algorithms provide a precise wildfire spread prediction accounting for the uncertainties in the model’s selected parameters.