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Dynamic Data-Driven Genetic Algorithm for forest fire spread prediction
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AI
DOI：10.1016/j.jocs.2012.06.002.png)
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Abstract
En 中文
This work represents the first step towards a Dynamic Data-Driven Application System (DDDAS) for wild-land 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. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
DDDAS
Forest fire
Simulation
Prediction quality
Genetic algorithm
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