PREDICTION OF DAM INFLOWS DURING SNOWMELT SEASON USING DEEP LEARNING
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- YAMADA Takashi
- 国立研究開発法人土木研究所 寒地土木研究所
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- ABE Masami
- いであ株式会社
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- TAKIGUCHI Hiroki
- いであ株式会社
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- KAKINUMA Takaharu
- 国立研究開発法人土木研究所 寒地土木研究所
Bibliographic Information
- Other Title
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- 深層学習を用いた融雪期のダム流入量予測
Description
<p> Accurate prediction of dam inflow during snowmelt is extremely important for disaster prevention and water use. Currently, AI is being utilized in the hydrological field, and research is being conducted on predicting river water levels and dam inflows. In this study, we used deep learning to predict dam inflows during the snowmelt season on an hourly basis. The results showed that reproducibility was high up to 24 hours ahead, but decreased after 24 hours. Therefore, it is considered that the practical limit of the forecast is 24 hours ahead. In addition, the effect of the number of intermediate layers was more significant than the effect of the normalization and standardization processes.</p>
Journal
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- Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering)
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Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 78 (2), I_151-I_156, 2022
Japan Society of Civil Engineers
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Details 詳細情報について
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- CRID
- 1390013408148645120
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- ISSN
- 2185467X
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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