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- W2520199001 abstract "Wildland fires at any location are a result of complex interactions between fuel, forest structure, topography, ignitions and weather conditions. Therefore, there is great interest in the development of computational tools for the prediction of these interactions during a wildland fire. A computational model that attempts to predict the spread of flames must provide a representation of the evolution of the fire over time. However, modelling any natural phenomenon is a difficult task, because models present a series of restrictions due to the need for a large number of input parameters. Moreover, there exist a degree of uncertainty in the input parameter values due to an inability to measure them in real time. To overcome this drawback and improve the quality of the prediction, several methods have been developed. This work presents a comparison of three methods for the reduction of uncertainty in the input parameter values, applied to the prediction of wildland fire behaviour. The three methods uses Parallel Evolutionary Algorithms to guide the search, Statistics to calibrate the results, and Parallelism to enhance the search in time and space terms." @default.
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- W2520199001 date "2016-07-01" @default.
- W2520199001 modified "2023-10-01" @default.
- W2520199001 title "Three evolutionary statistical parallel methods for uncertainty reduction in wildland fire prediction" @default.
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- W2520199001 doi "https://doi.org/10.1109/hpcsim.2016.7568406" @default.
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