Computational steering strategy to calibrate input variables in a dynamic data driven genetic algorithm for forest fire spread prediction

This work describes a Dynamic Data Driven Genetic Algorithm (DDDGA) for improving wildfires evolution prediction. We propose an universal computational steering strategy to automatically adjust certain input data values of forest fire simulators, which works independently on the underlying propagation model. This method has been implemented in a parallel fashion and the experiments performed demonstrated its ability to overcome the input data uncertainty and to reduce the execution time of the whole prediction process.