USE OF ARTIFICIAL NEURAL NETWORKS FOR MODELING INFLOW AND OUTFLOW AND SALINITY OF LAKE FETZARA IN THE REGION-ANNABA (NE ALGERIA)

Abstract

Lake Fetzara is one of the important lakes in the northeast of Algeria; the water supplying this lake comes from different precipitation and wadis. Moreover, Meboudja wadi constitutes the drainage channel. The water of the lake and the underlying groundwater is exposed to excessive overuse; which seriously threatens the hydrological and ecological balance. The overexploitation is explained by the increase in water mineralization, which poses a risk of soil salinization. To this end, this article deals with the subject of current salinity and predict its evolution over time by means of the modeling of the artificial neural network (ANN), according to the period of low water and the period of high water.  The ANN were trained using three different algorithms: the Scaled Conjugate Gradient back propagation (SCG) algorithm and One Step Secant back propagation (OSS) algorithm and Quasi-Newton algorithm (BFGS). The performance results indicate that the three algorithms provided satisfactory simulations according to the determination coefficient (R²) and the performance criteria of the mean square error (RMSE), with priority to the BFGS algorithm; where the coefficient of determination using the BFGS algorithm varies between 69.5% and 95.3%. The BFGS method presents better results in order to design appropriate institutional mechanisms, capable of leading to the protection of the quality of these resources essential to the promotion of sustainable development.