Parallel Multi-Objective GA Optimization For Distributed Watershed Model Parameters.

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  • 並列多目的遺伝的アルゴリズムによる分布型水循環モデルパラメータの最適化
  • ヘイレツ タモクテキ イデンテキ アルゴリズム ニ ヨル ブンプガタ ミズ ジュンカン モデル パラメータ ノ サイテキカ

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This paper describes an automatic optimization method for the 20 parameters of our distributed watershed model on Kokubunji experimental basin. Because this model simulates not only the groundwater runoff but also the groundwater levels at 6 observation wells, a multi-objective optimization method has to be employed. Even though GA requires more computa-tional load by increasing number of parameters and simulated areas, implementation of parallel computing is expected to re-duce the computational time. Multi-Objective Genetic Algorithm (MOGA), an effective global optimization method suitable for parallel computing, is examined in this paper. After calibrating GA parameters by numerical experiments, parallel MOGA is tested with observed data on Kokubunji experimental basin. The result shows its practical utility.

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