APPLICATION OF CONVOLUTIONAL NEURAL NETWORK TO OCCURRENCE PREDICTION OF HEAVY RAINFALL
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- SUZUKI Tsuguaki
- 京都大学 大学院工学研究科
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- KIM Sunmin
- 京都大学 大学院工学研究科
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- TACHIKAWA Yasuto
- 京都大学 大学院工学研究科
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- ICHIKAWA Yutaka
- 京都大学 大学院工学研究科
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- YOROZU Kazuaki
- 京都大学 大学院工学研究科
Bibliographic Information
- Other Title
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- 豪雨の発生予測に対する畳み込みニューラルネットワークの応用
Abstract
<p> A rainfall prediction model was proposed by applying Convolutional Neural Network to spatiotemporal two-dimensional data created from the time series information of meteorological observation at several sites. The rainfall threshold and the prediction lead time were set as the prediction setting. We used several meteorological variables as input data and studied a prediction model that does not use precipitation as input data. As the difference in the prediction accuracy in the prediction setting, it is confirmed that prediction accuracy decreases as the prediction lead time becomes longer, and rainfall prediction becomes more difficult as the threshold becomes higher. From the prediction model without precipitation, it turned out that the information of precipitation had an influence on the model accuracy, and it was suggested that other meteorological variables also contain information related to rainfall prediction.</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) 74 (5), I_295-I_300, 2018
Japan Society of Civil Engineers
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Details 詳細情報について
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- CRID
- 1390283659823203840
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- NII Article ID
- 130007757781
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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
- CiNii Articles
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- Abstract License Flag
- Disallowed