Statistical modelling of suspended sediment transport in the Cherf drainage basin, Algeria

This work deals with the main topic of the assessment of the Cherf drainage basin sediment yield based on available water discharge and suspended sediment concentration observations, and on the application of general and multivariate models. This study is also part of a broader effort aiming to predict reservoir siltation and future reliability. The 19 years of available sediment concentration data (1975/1976-1993/1994) is used to predict suspended sediment loads. From 1994, this drainage basin has shown a construction of a reservoir at its outlet (Cherf dam) for civil and industrial use. From that date, only monthly water volumes are used to estimate sediment yield in the reservoir. The methodology involved in this study is developed by a conventional sediment rating curve and a multiple regression model. The former method is investigated with the mean discharge classes derived from the recorded suspended sediment concentrations and water discharges for the Cherf drainage basin (1710 km²), prior to the reservoir construction. The later is based on rock type erodibility, mean annual runoff and basin area variables, and which is applied for the ungauged reservoir basin of 1735 km². For the rating curve model, a regression analysis is made between the instantaneous suspended sediment concentration (C) and the instantaneous water discharge (Q) based on all recorded data and seasonal ratings. Optimization of rating curve method is validated by comparing the predicted against observed values on scatter plots

Zastosowanie sztucznych sieci neuronowych do przewidywania ładunku zawiesiny; przypadek zlewni rzeki Mellah w północno-wschodniej Algierii

In this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.W niniejszej pracy przedstawiono działanie najlepszego algorytmu sieci neuronowych z użyciem wielowarstwowego perceptronu do przewidywania odpływu zawiesiny ze zlewni rzeki Mellah. Do treningu i testowania sieci użyto serii czasowych dobowego odpływu zawiesiny i odpływu wody z profilu wodowskazowego Bouchegouf. Do oceny działania modelu wykorzystano szereg parametrów statystycznych, takich jak pierwiastek ze średniego błędu kwadratowego, średni błąd bezwzględny, współczynnik wydajności i współczynnik determinacji. Model dawał zadowalające wyniki i wykazywał bardzo dobrą zgodność między obserwowanymi i przewidywanymi danymi. Wyniki świadczą także, że model jest w stanie wychwycić szczegółowy wzorzec odpływu zawiesiny ze zlewni rzeki Mellah