IMPROVEMENT OF DEEP LEARNING RAINFALL FORECAST AND DETAILED EVALUATION
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- KANEKO Ryo
- 東京大学 生産技術研究所
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- YOSHIMURA Kei
- 東京大学 生産技術研究所
Bibliographic Information
- Other Title
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- 深層学習降水予測の精度改善と現状の詳細評価
Abstract
<p> Short-term rainfall prediction using deep learning has gained attention recently. However, the evaluation of forecast accuracy has been limited, and comparisons with operational models are rare. In this study, we improved an existing forecasting method for rainfall prediction in Japan up to 6 hours ahead and comprehensively evaluated the results. The proposed method outperforms operational forecasts for precipitation exceeding 50 mm h-1 and 5 mm h-1, and the results indicated that the possibility that the model learn the physical phenomena, which the operational model could not consider. However, even during the winter season, there are events caused by low-pressure-induced rainfall and other similar characteristics to summer rainfall, which can be predicted. These findings suggest the need for further discussions on constructing an accurate model and building the dataset appropriately.</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
- 1390299318851713408
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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