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CONTRIBUTION DE L'APPRENTISSAGE AUTOMATIQUE DANS L'ESTIMATION DES DÉBITS MENSUELS DU FLEUVE BAGOÉ À LA STATION HYDROMÉTRIQUE DE KOUTO AU NORD-OUEST DE LA CÔTE D'IVOIRE
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CONTRIBUTION DE L'APPRENTISSAGE AUTOMATIQUE DANS L'ESTIMATION DES DÉBITS MENSUELS DU FLEUVE BAGOÉ À LA STATION HYDROMÉTRIQUE DE KOUTO AU NORD-OUEST DE LA CÔTE D'IVOIRE
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CONTRIBUTION DE L'APPRENTISSAGE AUTOMATIQUE DANS L'ESTIMATION DES DÉBITS MENSUELS DU FLEUVE BAGOÉ À LA STATION HYDROMÉTRIQUE DE KOUTO AU NORD-OUEST DE LA CÔTE D'IVOIRE
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Journal Article
CONTRIBUTION DE L'APPRENTISSAGE AUTOMATIQUE DANS L'ESTIMATION DES DÉBITS MENSUELS DU FLEUVE BAGOÉ À LA STATION HYDROMÉTRIQUE DE KOUTO AU NORD-OUEST DE LA CÔTE D'IVOIRE
2025
Overview
Streamflow data are very important in assessing the groundwater and surface water resources of a given region. In northern Côte d'Ivoire, particularly in the Bagoé region, although there are long series of rainfall data, streamflow data are still scarce. The few chronicles available are very short and incomplete. The aim of this study is to obtain a long flow chronicle for the period 1996-2016. It aims to estimate flows in the Bagoé River at the Kouto hydrometric station using neural networks. To this end, two neural models were developed to estimate variations in monthly flows of the Bagoé River from 1996 to 2016. The modeling was validated using the Nash criterion (%), the Pearson coefficient (R), the maximum flow ratio and the robustness criterion. The results showed that the validation criteria for these models are optimal. The Nash criterion is greater than 84% for both calibration and validation. The Pearson coefficient ranged from 92% to 96% in calibration and validation. The maximum flow ratio ranges from 93% to 118% in calibration and validation. The robustness criterion ranged from 2.91% to 7.62%. All these results reflect the good performance and stability of neural network-based models for estimating flows in the Bagoé river.
Publisher
International Journal of Innovation and Applied Studies
Subject
