Suchergebnisse

Knowledge of sediment yield and the factors controlling it provides useful ‎information for estimating ‎erosion intensities within river basins. The objective of ‎this study was to build a model from which ‎suspended sediment yield could be ‎estimated from ungauged rivers using computed sediment yield and ‎physical ‎factors. Researchers working on suspended sediment transported by wadis in the ‎Maghreb are ‎usually facing the lack of available data for such river types. Further ‎study of the prediction of sediment ‎transport in these regions and its variability is ‎clearly required. In this work, ANNs were built between ‎sediment yield ‎established from longterm measurement series at gauging stations in Algerian ‎catchments and ‎corresponding basic physiographic parameters such as rainfall, ‎runoff, lithology index, coefficient of ‎torrentiality, and basin area. The proposed ‎Levenberg-Marquardt and Multilayer Perceptron algorithms to ‎train the neural ‎networks of the current research study was based on the feed-forward ‎backpropagation ‎method with combinations of number of neurons in each hidden ‎layer, transfer function, error goal. ‎Additionally, three statistical measurements, ‎namely the ‎root mean square error (RMSE), ‎the coefficient of ‎determination (R²), ‎and the efficiency factor (EF)‎ have been reported for ‎examining the forecasting ‎‎accuracy of the developed model.‎ Single plot displays of network outputs with ‎respect to targets for training ‎have provided good performance results and good ‎fitting . Thus, ANNs were a promising method for ‎predicting suspended sediment ‎yield in ungauged Algerian catchments.‎

Soil erosion is one of the problems threatening the Algerian environment. In agriculture, soil erosion leads to the thinning of the topsoil under the effect of the natural erosive forces of water, or under the effect of agricultural activities. The present study aims to estimate average soil loss rate and to identify vulnerable zones. Through the integration of RUSLE model at the Saf Saf watershed, various parameters are utilized such as the rainfall erosivity factor (R), soil erodibility factor (K), slope length - slope factor (LS), crop management factor (C) and practice management factor (P). All these parameters are prepared and processed through a geographic information system (GIS) and remote sensing using various database sources. The results reveal that the river basin has an average annual soil loss of 3.9 t ha−1 yr−1, and annual soil loss of 4.53 million tonnes for the period 1975-2017. Meanwhile, eighty five percent of the study area is experiencing acceptable rate of soil erosion loss, which is ranging between 0 to 5 t ha−1 yr−1. The present study of risk assessment can contribute to understand the spatial pattern of soil erosion in order to use appropriate conservation practices for sustainable soil management.