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In Computational Science and Engineering, 2008. CSE '08. 11th IEEE International Conference on (July 2008), pp. 275-282, doi:10.1109/cse.2008.15 Key: citeulike:12031487
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In this paper, we propose a combination of two dynamic data driven application system (DDDAS) methodologies to predict wildfirespsila propagation. Our goal is to build a system that dynamically adapts to constant changes in environmental conditions when a hazard occurs and under strict real-time deadlines. For this purpose, we are on the way of building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time.
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