APPLICATION OF DEEP LEARNING TO DAM INFLOW FORECASTS WITH DIFFERENT CHARACTERISTICS IN THE CHUBU AREA AND THE EFFECT OF FORECAST ACCURACY CAUSED BY MIX OF INPUT RAINFALL TYPES
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- KUREBAYASHI Toshiaki
- (株)建設技術研究所 中部支社 河川部
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- TSUJIKURA Hiroki
- (株)建設技術研究所 中部支社 河川部
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- TAKEDA Eisuke
- (株)建設技術研究所 中部支社 河川部
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- KANIE Morihito
- (株)建設技術研究所 中部支社 河川部
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- MATSUBARA Mitsuyuki
- 国土交通省 中部地方整備局 河川部
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- FUNATO Nobuhisa
- 国土交通省 中部地方整備局 河川部
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- IDE Kota
- 国土交通省 中部地方整備局 河川部
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- ASANO Masahiro
- 国土交通省 中部地方整備局 河川部
Bibliographic Information
- Other Title
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- 中部地方の特性が異なるダム流入量予測への深層学習の適用と学習用入力雨量の種別混在による予測精度への影響
Description
<p> We classified dams in the Chubu region into three patterns with similar characteristics in the upstream areas, based on the influence of upstream dam discharge operations. By constructing deep learning models for representative dam in each pattern and evaluating their prediction accuracy, we assessed the validity of the model construction method based on the input conditions and its applicability to predicting dam inflow volume. To address the challenge of limited data for low-frequency and unprecedented floods, we supplemented the training data with large-scale flood events during periods with-out radar rainfall data, utilizing ground-based rainfall data. Additionally, we analyzed the impact of using different types of rainfall data in training, verifying the effectiveness of our approach.</p>
Journal
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- Japanese Journal of JSCE
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Japanese Journal of JSCE 80 (16), n/a-, 2024
Japan Society of Civil Engineers
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Details 詳細情報について
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- CRID
- 1390299318851732224
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- ISSN
- 24366021
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
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- Abstract License Flag
- Disallowed