Dynamic Data-Driven Genetic Algorithm for forest fire spread prediction

This work represents the first step towards a Dynamic Data-Driven Application System (DDDAS) for wildland fire prediction. Our main efforts are focused on taking advantage of the computing power provided by High Performance Computing systems and to propose computational data-driven steering strategies to overcome input data uncertainty. In doing so, prediction quality can be enhanced significantly. On the other hand, these proposals reduce the execution time of the overall prediction process in order to be of use during real-time crisis. In particular, this work describes a Dynamic Data-Driven Genetic Algorithm (DDDGA) used as steering strategy to automatically adjust highly dynamic input data values of forest fire simulators taking into account the underlying propagation model and real fire behaviour.

► We present a Dynamic Data-Driven Application System for forest fire prediction.
► Its computational steering method is completely simulator independent.
► Highly dynamic input parameters are adjusted to improve predictions.
► Enhancements to the applied GA significantly reduce overall prediction time.