Relieving Uncertainty in Forest Fire Spread Prediction by Exploiting Multicore Architectures

The most important aspect that affects the reliability of environmental simulations is the un- certainty on the parameter settings describing the environmental conditions, which may involve important biases between simulation and reality. To relieve such arbitrariness, a two-stage pre- diction method was developed, based on the adjustment of the input parameters according to the real observed evolution. This method enhances the quality of the predictions, but it is very demanding in terms of time and computational resources needed. In this work, we describe a methodology developed for response time assessment in the case of fire spread prediction, based on evolutionary computation. In addition, a parallelization of one of the most used fire spread simulators, FARSITE, was carried out to take advantage of multicore architectures. This al- lows us to design proper allocation policies that significantly reduce simulation time and reach successful predictions much faster. A multi-platform performance study is reported to analyze the benefits of the methodology