Modeling River Discharge using Deep Learning in the Ouémé catchment at Savè outlet (Benin, West Africa)

International Journal of Geoinformatics and Geological Science

© 2023 by SSRG - IJGGS Journal

Volume 10 Issue 1

Year of Publication : 2023

Authors : Zohou Pierre Jérôme, Biao Iboukoun Eliézer, Aoga John, Houessou Oscar, Alamou Adéchina Eric, Eugène C.Ezin

10.14445/23939206/IJGGS-V10I1P103

How to Cite?

Zohou Pierre Jérôme, Biao Iboukoun Eliézer, Aoga John, Houessou Oscar, Alamou Adéchina Eric, Eugène C.Ezin, "Modeling River Discharge using Deep Learning in the Ouémé catchment at Savè outlet (Benin, West Africa)," SSRG International Journal of Geoinformatics and Geological Science, vol. 10,  no. 1, pp. 29-35, 2023. Crossref, https://doi.org/10.14445/23939206/IJGGS-V10I1P103

Abstract:

This paper presents a modeling approach based on Artificial Neural Networks (ANNs) in the Ouémé river catchment at Savè. To do this, we used precipitation data as input over the period 1965 -2010 to simulate river discharge in the study area by using two ANNs models such as the Long Short Term Memory (LSTM) and Recurrent Gate Networks (GRU) models. Indeed, the description of the stochastic nature of the data is better presented today by ANNs models than the statistical models. We compared the performance of these two models based on different evaluation criteria. The predictions made using these models show a strong similarity between the observed and simulated flows. The deep learning models gave good results. Indeed, in calibration and validation, the Nash Sutcliffe Efficiency (NSE) and the coefficient of determination (R²) are very close to one (calibration: R²= 0.995, NSE= 0.991, and RMSE= 0.18; validation: R² = 0.975, NSE= 0.971, and RMSE= 0.41). This good performance of LSTM and GRU confirms the importance of models based on Artificial Intelligence in modeling hydrological phenomena for better decision-making.

Keywords:

Artificial Neural Networks, Modeling, Ouémé catchment at Savè, Long Short Term Memory, Gated Recurrent Unit.

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