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Yokoo, K., Ishida, K., Ercan, A., Tu, T., Nagasato, T., Kiyama, M. et al. (2021). Capabilities of Deep Learning Models on Learning Physical Relationships: Case of Rainfall-Runoff Modeling with LSTM. Science of the Total Environment, 802, Article ID: 149876.
https://doi.org/10.1016/j.scitotenv.2021.149876

has been cited by the following article:

• TITLE: Comparison of Two Recurrent Neural Networks for Rainfall-Runoff Modeling in the Zou River Basin at Atchérigbé (Bénin)

  AUTHORS: Iboukoun Eliézer Biao, Oscar Houessou, Pierre Jérôme Zohou, Adéchina Eric Alamou

  KEYWORDS: Supervised Learning, Modeling, Zou Basin, Long and Short-Term Memory, Gated Recurrent Unit, Hyperparameters Optimization

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.12 No.9, September 27, 2024

  ABSTRACT: Hydrological models are developed to simulate river flows over a watershed for many practical applications in the field of water resource management. The present paper compares the performance of two recurrent neural networks for rainfall-runoff modeling in the Zou River basin at Atchérigbé outlet. To this end, we used daily precipitation data over the period 1988-2010 as input of the models, such as the Long Short-Term Memory (LSTM) and Recurrent Gate Networks (GRU) to simulate river discharge in the study area. The investigated models give good results in calibration (R2 = 0.888, NSE = 0.886, and RMSE = 0.42 for LSTM; R2 = 0.9, NSE = 0.9 and RMSE = 0.397 for GRU) and in validation (R2 = 0.865, NSE = 0.851, and RMSE = 0.329 for LSTM; R2 = 0.9, NSE = 0.865 and RMSE = 0.301 for GRU). This good performance of LSTM and GRU models confirms the importance of models based on machine learning in modeling hydrological phenomena for better decision-making.