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Comparison of Two recurrent Neural Networks for rainfall-runoff Modeling in the Zou river Basin at Atchérigbé (Bénin)

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journal of Geoscience and Environment Protection 2024年第9期12卷 167-181页

作者： Iboukoun Eliézer Biao Oscar Houessou pie.re.j.rôme zohou Adéchina Eric Alamou National School of Mathematical Engineering and Modeling National University of Sciences Technology Engineering and Mathematics Abomey Benin LaGEA National School of Public Works National University of Sciences Technology Engineering and Mathematics Abomey Benin International Chair of Mathematical Physics and Applications (ICMPA-UNESCO Chair) University of Abomey-Calavi Abomey-Calavi Bnin

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.

关键词： Supervised Learning Modeling Zou Basin Long and Short-Term memory Gated recurrent Unit Hyperparameters Optimization

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An Automatic Optimization Technique for the Calibration of a Physically Based Hydrological rainfall-runoff Model

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journal of Geoscience and Environment Protection 2021年第3期9卷 1-20页

作者： zohou pie.re.j.rôme Alamou Adéchina Eric Obada Ezéchiel Biao Iboukoun Eliézer Eugène C. Ezin Université d’Abomey-Calavi (UAC) Abomey-Calavi Bénin Laboratoire de recherche en Sciences Informatiques et Applications (LrSIA) UAC Abomey-Calavi Bénin Laboratoire d’Hydrologie Appliquée (LHA) UAC Abomey-Calavi Bénin Université Nationale des Sciences Technologies Ingénierie et Mathématiques (UNSTIM) Abomey Bénin

Models are tools widely used in the prediction of hydrological phenomena. The present study aims to contribute to the implementation of an automatic optimization strategy of parameters for the calibration of a hydrological model based on the least action principle (HyMoLAP). The Downhill Simplex method is also known as the Nelder-mead algorithm, which is a heuristic research method, is used to optimize the cost function on a given domain. The performance of the model is evaluated by the Nash Stucliffe Efficiency Index (NSE), the root mean Square Error (rMSE), the coefficient of determination (r2), the mean Absolute Error (MAE). A comparative estimation is conducted using the Nash-Sutcliffe Modeling Efficiency Index and the mean relative error to evaluate the performance of the optimization method. It appears that the variation in water balance parameter values is acceptable. The simulated optimization method appears to be the best in terms of lower variability of parameter values during successive tests. The quality of the parameter sets obtained is good enough to impact the performance of the objective functions in a minimum number of iterations. We have analyzed the algorithm from a technical point of view, and we have carried out an experimental comparison between specific factors such as the model structure and the parameter’s values. The results obtained confirm the quality of the model (NSE = 0.90 and 0.75 respectively in calibration and validation) and allow us to evaluate the efficiency of the Nelder-mead algorithm in the automatic calibration of the HyMoLAP model. The developed hybrid automatic calibration approach is therefore one of the promising ways to reduce computational time in rainfall-runoff modeling.

关键词： Optimization HyMoLAP Automatic Calibration Validation Nelder-mead Algorithm

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