Evolutionary-statistical system for uncertainty reduction problems in wildfires

Abstract

Fire modelling is used by engineers and scientists to understand and to predict possible fire behaviour. Empirical, semi-empirical, and physical models have been developed to predict wildfire behaviour. Any of these can be used to develop simulators and tools for preventing and fighting wildfires. However, in many cases the models present a series of limitations related to the need for a large number of input parameters. Moreover, such parameters often have some degree of uncertainty due to the impossibility of getting all of them in real time. Consequently, these values have to be estimated from indirect measurements, which negatively impacts on the output of the model. In this paper we show a method which takes advantage of the computational power provided by High Performance Computing to improve the quality of the output of the model. This method combines Statistical Analysis with Parallel Evolutionary Algorithms. Besides, we compare this method with a previous version which did not use evolutionary algorithms.