Evolutionary Intelligent System for input parameter optimisation in environmental modelling: A case study in forest fire forecasting
作者： Kerstin Wendt , Ana Cortes , Tomas Margalef
关键词: Artificial intelligence 、 Evolutionary computation 、 Mathematical optimization 、 Fire spread 、 Calibration (statistics) 、 Genetic algorithm 、 Computer science 、 Task (project management) 、 Cluster analysis 、 Environmental modelling 、 Machine learning
摘要: The need for input parameter optimisation in environmental modelling is a long-known and very time-consuming task. However, to avoid tragedy, disaster propagation predictions have satisfy hard real-time constraints. Especially small control centres with limited computing resources require fast efficient calibration methods deliver reliable time. combination of clustering method together Genetic Algorithm used as technique forest fire spread prediction. We formalise demonstrate the potential resulting Evolutionary Intelligent System's architecture solve complex problem on restricted simulation conditions.
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