Comparison of three recurrent neural networks for rainfall-runoff modelling at a snow-dominated watershed

Date

2021-10-25

Author

Yokoo, K.
Ishida, K.
Nagasato, T.
Ercan, Ali
Tu, T.

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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© Published under licence by IOP Publishing Ltd.In recent years, rainfall-runoff modelling using LSTM has shown high adaptability. However, LSTM requires far more computational costs than traditional RNN. In addition, a different type of RNN, GRU, has been developed to solve this issue of LSTM. Therefore, this study compares the accuracy of the deep learning methods for rainfall-runoff modelling using three deep learning methods in a snow-dominated area. Besides, the setting of hyperparameters may affect accuracy. The accuracy of these deep learning methods was investigated by trying multiple combinations of hyperparameters. The input data were daily temperature data and precipitation data. The results show that GRU gives the highest accuracy in most combinations.

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85118956686&origin=inward
https://hdl.handle.net/11511/100716

DOI

https://doi.org/10.1088/1755-1315/851/1/012012

Conference Name

2021 International Conference on Geological Engineering and Geosciences, ICGoES 2021

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Department of Civil Engineering, Conference / Seminar

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Citation Formats

K. Yokoo, K. Ishida, T. Nagasato, A. Ercan, and T. Tu, “Comparison of three recurrent neural networks for rainfall-runoff modelling at a snow-dominated watershed,” Yogyakarta, Virtual, Endonezya, 2021, vol. 851, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85118956686&origin=inward.