Injecting Dynamic Real-time Data into a DDDAS for Forest Fire Behavior Prediction?

Abstract. This work presents a novel idea for forest fire prediction, based on Dynamic Data Driven Application Systems. We developed a system capable of assimilating data at execution time, and conduct sim-ulation according to those measurements. We used a conventional sim-ulator, and created a methodology capable of removing parameter un-certainty. To test this methodology, several experiments were performed based on southern California fires