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

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

2021 International Conference on Geological Engineering and Geosciences, ICGoES 2021, Yogyakarta, Virtual, Endonezya, 16 - 18 Mart 2021, cilt.851 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 851
  • Doi Numarası: 10.1088/1755-1315/851/1/012012
  • Basıldığı Şehir: Yogyakarta, Virtual
  • Basıldığı Ülke: Endonezya
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

© 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.