Accuracy Improvement of Dam Inflow Discharge Forecasting by Applying Gradient Boosting, Yachiyo Engineering Co., Ltd.
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- AMAKATA Masazumi
- 八千代エンジニヤリング株式会社 技術創発研究所
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- FUJII Junichiro
- 八千代エンジニヤリング株式会社 技術創発研究所
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- YANADA Nobuka
- 八千代エンジニヤリング株式会社 北日本支店
Bibliographic Information
- Other Title
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- Gradient Boostingの適用によるダム流入量予測の精度向上
- Gradient Boosting ノ テキヨウ ニ ヨル ダム リュウニュウリョウ ヨソク ノ セイド コウジョウ
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Abstract
<p>The strong rainfall which we have never assumed in the river design happens frequently and many water and sediment disasters happen. In order to minimize these damages, the dams which are able to control the flood damages are managed to maximize the reservoir utilization based on the regulations of Pre-discharge operation and Disaster prevention operation in the time of abnormal flood. But when we are going to aim at maximizing the reservoir utilization more effectively, it is indispensable to improve the accuracy of dam inflow forecasting. In this thesis, we will show that the ensemble machine learning method called Gradient Boosting is much more accuracy than the neural network method when we use them for the dam inflow forecasting scheme.</p>
Journal
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- Journal of Japan Society of Dam Engineers
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Journal of Japan Society of Dam Engineers 30 (1), 18-27, 2020-03-15
Japan Society of Dam Engineers
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Details 詳細情報について
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- CRID
- 1390565134840123136
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- NII Article ID
- 130007815458
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- NII Book ID
- AN10442669
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- ISSN
- 18808220
- 09173145
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- NDL BIB ID
- 030323695
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- CiNii Articles
- Crossref
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- Abstract License Flag
- Disallowed