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Title
Japanese:全球気候ビッグデータと深層学習によるチャオプラヤー川流域S2S降雨予測 
English: 
Author
Japanese: 長谷川 青春, 鼎 信次郎.  
English: Kiyoharu Hasegawa, Shinjiro Kanae.  
Language Japanese 
Journal/Book name
Japanese:土木学会論文集B1（水工学） 
English:Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 
Volume, Number, Page Volume 77    Issue 2    p. I1207-I1212
Published date Dec. 2021 
Publisher
Japanese:公益社団法人土木学会 
English:Japan Society of Civil Engineers 
Conference name
Japanese:第66回水工学講演会 
English:The 66th Conference on Hydraulic Engineering 
Conference site
Japanese:オンライン 
English:Online 
Official URL https://www.jstage.jst.go.jp/article/jscejhe/77/2/77_I_1207/_article/-char/ja/
 
DOI https://doi.org/10.2208/jscejhe.77.2_I_1207
Abstract The accuracy of climate models decreases drastically when predicting rainfall for more than two weeks, so it is expected that deep learning models can be applied. In this study, deep learning is applied to the Chao Phraya River basin in Thailand to predict rainfall for one to three months during the rainy season. We examined whether the lack of observational data can be solved by using the CMIP5 dataset, which is a non-observational dataset. The results show that CMIP5 can be adapted to the training data. Furthermore, we found that deep learning methods tend to outperform climate models when the prediction lead time is long.

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