土木学会論文集B1(水工学)
Online ISSN : 2185-467X
ISSN-L : 2185-467X
水工学論文集第60巻
深層学習を用いた河川水位予測手法の開発
一言 正之櫻庭 雅明清 雄一
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2016 年 72 巻 4 号 p. I_187-I_192

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 The real-time river stage prediction model is developed, using the artificial neural network model which is trained by the deep learning method. The model is composed of 4 layer feed-forward network. As a network training method, stochastic gradient descent method based on the back propagation method was applied. As a pre-training method, the denoising autoencoder was applied. The developed model is applied to the one catchment of the OOYODO River, one of the first-grade river in Japan. Input of the model is hourly change of water level and hourly rainfall, output data is water level of HIWATASHI. To clarify the suitable configuration of the model, case study was done. The prediction result is compared with the other prediction models, consequently the developed model showed the best performance.
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© 2016 公益社団法人 土木学会
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