法政大学情報メディア教育研究センター研究報告
Online ISSN : 1882-7594
機械学習による流域水収支モデルのパラメータの自動最適化
沼尻 治樹
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研究報告書・技術報告書 フリー

2021 年 36 巻 p. 42-46

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The study by Numajiri [1] well reproduced the seasonal variation of river runoff by a monthly basin water balance model using a distributed tank model. The search for the optimal parameters is important for the construction of this distributed tank model. However, the method is not easy. When Sugawara [2] invented the tank model, Sugawara gave up the automatic search for the optimum parameters, citing one of the reasons that the computing power of the computer takes time. Numajiri [1] attempted automatic optimization by the conditional brute force attack method using a personal computer with improved performance. The purpose of this study is to improve the efficiency of optimal parameter exploration by using a machine learning library.
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