The annual sediment load of a river is generally determined either from direct measurements of the sediment load throughout the year or from any of the many sediment transport equations that are available today. Due to lack of a long-term sediment concentration data, sediment rating curves and flux estimation are the most widely applied. This paper has investigated the abilities of statistical models to improve the accuracy of streamflow-suspended sediment relationships in daily and annual suspended sediment estimation. In this study, a comparison was made between suspended sediment rating curves and artificial neural networks (ANNs) for the El Kebir catchment. Daily water discharge and daily suspended sediment data from the gauging station of Ain Assel, were used as inputs and targets in the models which were based on the cascade-forward and feed-forward back-propagation using Levenberg-Marquardt and Bayesian regularization algorithms. The model results have shown that the ANN models have the highest efficiency to reproduce the daily sediment load and the global annual sediment yields. Our estimation based on the available data indicated that the areas along the El Kebir River have experienced high sediment fluxes that could have obvious impacts on the sediment trapping and siltation in the Mexa reservoir.

Many studies on sediment transport have been carried out on Algerian rivers but few studies have been undertaken in catchments of the Northeast of Algeria. The scarcity or discontinuity on sediment transport measurements reduces knowledge about soil loss. In some cases, researchers find often difficulties to apply the most suitable methods to estimate sediment load. The present work represents an assessment of suspended sediment yield from the Saf Saf catchment (322 km 2) over 39 years. Long-term annual suspended sediment loads are estimated using non-linear power model, developed on mean discharge class technique as a sediment rating curve. There is a challenge to estimate suspended sediment load in the Saf Saf catchment, which is distinguished by rapid discharge variation. A second aim is to examine monthly and annual variations in discharge, suspended concentration, rainfall and load in this river and to find causes for these variations. The results show that the mean annual sediment yield is equal to 477 T km-2 yr-1 during the study period. Moreover, the long term variability analysis of sediment load seems to be very high from year to year depending on climatic conditions. The analysis of annual sediment load shows a decreasing trend along 39 years, mainly from 1997. Most sediment loads are transported during the winter season, which represents 78% of the total sediment load. The understanding of sediment transport relationships gained from this study should provide a good starting point for researchers and policymakers to begin addressing sediment issues within the catchment.