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 simulation according to those measurements. We used a conventional simulator, and created a methodology capable of removing parameter uncertainty. To test this methodology, several experiments were performed based on southern California fires. © 2009 Springer Berlin Heidelberg.