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.