Multi-model multi-scale, a coupled system for forest fire spread prediction

By Pau Costa Foundation on

Session: Fire response

Presenter: Ana Cortes Fite (High Performance Computing Applications for Science and Engineering (HPCA4SE), Universitat Autònoma de Barcelona)

Contact email: ana.cortes@uab.cat

HPCA4SE research group is focused on how to apply High Performance Computing both to science and engineering fields considering basic and applied research. The HPC platforms used are multi/many core systems, GPUs, distributed systems, supercomputers etc… The models and tools developed provide system performance improvements and also facilitate the use of these systems. The HPCA4SE group has been working on grand challenges related to bioinformatics, natural hazards, engineering structures and so on…

Link: http://grupsderecerca.uab.cat/hpca4se/en/content/inici

ABSTRACT: Forest fires are a kind of natural hazard with a high number of occurrences in southern European countries. To avoid major damages and to improve forest fire management, one can use forest fire spread simulators to predict fire behavior. When providing forest fire predictions, there are two main considerations: accuracy and computation time. In the context of natural hazards simulation, it is well known that part of the final forecast error comes from uncertainty in the input data. These data typically consist of a set of GIS files, which should be appropriately conflated. For this reason, several input data calibration methods have been developed by the scientific community. The Two-Stage calibration methodology, which is based on improving the near future forecast based on the near past observation of the fire behavior, can help to enhanced forest fire spread predictions frameworks. This calibration strategy is computationally intensive and time-consuming because it uses a Genetic Algorithm as a solution. Taking into account the aspect of urgency in forest fire spread prediction, it is necessary to maintain a balance between accuracy and the time needed to calibrate the input parameters. In order to take advantage of this technique, one must deal with the problem that some of the obtained solutions are impractical, since they involve simulation times that are too long, preventing the prediction system from being deployed at an operational level.  Since the system requires to coupled multiple models: forest fire spread model, meteorological model, wind field model, vegetation models… and so on, an accurate analysis of how this multi-model multi-scale system must be joined to became a cyberinfrastructure to enable forest fire spread prediction at real time, will be described.

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