One of the challenges still open to wildland fire simulators is the capacity of working under real-time constrains with the aim of providing fire spread predictions that could be useful in fire mitigation interventions. In this paper, a parallel optimization framework for improving wildland fire prediction is applied to a real laboratory fire. The proposed prediction methodology has been tested on a Linux cluster using MPI. © Springer-Verlag Berlin Heidelberg 2003.
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 1 Dec 2003|