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Accueil du site → Doctorat → Algérie → 2017 → CONTRIBUTION TO THE CHARACTERIZATION AND THE MODELLING OF SEDIMENT TRANSPORT IN URBAN HYDRO-SYSTEMS

Université Hassiba Benbouali de Chlef (2017)

CONTRIBUTION TO THE CHARACTERIZATION AND THE MODELLING OF SEDIMENT TRANSPORT IN URBAN HYDRO-SYSTEMS

TACHI, Salah Eddine

Titre : CONTRIBUTION TO THE CHARACTERIZATION AND THE MODELLING OF SEDIMENT TRANSPORT IN URBAN HYDRO-SYSTEMS

Auteur : TACHI, Salah Eddine

Université de soutenance : Université Hassiba Benbouali de Chlef

Grade : Doctorat Construction Hydraulique 2017

Résumé
In the management of water resources in different hydro- systems it is important to evaluate and predict the sediment load in rivers. It is difficult to obtain an effective and fast estimation of sediment load by Artificial Neural Network without avoiding over-fitting of the training data. The presented thesis comprises of three steps in order to obtain an Artificial Neural Network model. In the first step the study comprises the comparison of a multi-layer perception network one with non-regularized network and the other with regularized network using the Early Stopping technique to estimate and forecast suspended sediment load in the Isser River, upstream of Beni Amran reservoir, northern Algeria. The study was carried out on daily sediment discharge and water discharge data of 30 years (1971–2001). In the second step, the author used the same Artificial Neural Network model once again, using non regularized and then regularized model to forecast suspended sediment in the Sebaou Wadi, in the Great Kabyle watershed, northern Algeria. The study was conducted on daily water and sediment discharge data of 7 years between (1978 and 1989). Both studies on different valleys were compared using the regularized and non regularized neural networks. The models were evaluated in terms of the Coefficient of Determination (R²) and the Root Mean Square Error (RMSE). The comparison results indicated that the regularizing neural network using the Early Stopping criterion to avoid over fitting performs better than the non regularized networks in both studied areas, with a priority of a better performance values to the application of the Isser Wadi. The results show that the overtraining in the back propagation occurs because of the complexity of the data introduced to the network. In the third step authors tried to confirm the efficiency of their neural network model using the Early Stopping technique, the application of the neural network model was the prediction of suspended sediment discharge in un-gauged river. The study was applied on two different sites, firstly, we used the input data of the Isser Wadi to forecast the suspended sediment in the Sebaou Wadi, carried on daily water and sediment discharge in a period of 7 years (9 years using training inputs from the Isser Wadi, and two years for validation and testing depending on the data of the Sebaou Wadi). Secondly, we used the input data of the Sebaou Wadi to forecast the sediment discharge of the Isser Wadi during the period of 7 years with the same divided data sets as the previous application on the Sebaou Wadi. The comparison of the results indicated that the over-fitting occurred often in our models, and the Early Stopping technique showed acceptable values but still further from the applications using real river data that were shown in the first and second 7 steps. The use of the early stopping technique in forecasting sediment discharge is very effective and robust especially to avoid the over-fitting that occurred often in our models.

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Page publiée le 25 septembre 2018