estimation suspended sediment load with sediment rating curve and artificial neural network method (case study: lorestan province)

suspended sediment estimation is an important factor from different aspects including, farming, soil conservation, dams, aquatic life, as well as various aspects of the research. there are different methods for suspended sediment estimation. this study aims to estimate suspended sediment using feed forward neural network with error back propagation with levenberg-marquardt back propagation algorithm and compare the results with best sediment rating curves among commonly used sediment rating curves, including: linear, seasonal, monthly and mean load within discharge classes. to attain this, the sediment discharge and the corresponding water discharge data for ten hydrometric stations of lorestan province of iran were used. in next step different methods of sediment rating curves along with different correction factors, a total of 20 methods were applied to data. results showed among examined methods; monthly rating curve with muve correction factor has been selected as best, based on nash and sutcliffe index and accuracy index. then results of estimating sediment load by using selected sediment rating curve were compared with the results of the neural network. mean-square error and nash and sutcliffe index were applied to select more appropriate method. the results showed the suitability of the feed forward neural network error propagation in compare with sediment rating curves.