Sediment assessment for a watershed in arid region via neural networks

In the present study, the estimation of suspended sediment load is computed by four Artificial Neural Networks (ANNs) algorithms, Cascade Forward Back Propagation (CFBP), Feed Forward Back Propagation (FFBP), Radial Basis Function (RBF), and Recurrent Neural Network (RNN). Five cases of model input are calibrated to establish the relationship among precipitation, discharge and suspended sediment load. While discharge and rainfall up to four previous days as employed for input, model gives pre-eminent performance. Sensitivity of all models is appraised concerning Nash-Sutcliffe coefficient (E NS ) and coefficient of determination (R 2 ) for predicting sediment load. Among all ANNs, MMF (Morgan-Morgan-Finney) model when trained with stream flow as the input in RNN, gives best result with coefficient of determination, R 2 as 0.9474, while the values for FFBP, CFBP and RBF are 0.9115, 0.8766 and 0.8511, respectively. Performance of all results show that an MMF model is superior to conventional SRC (Sediment Rating Curve) and MLR (Multiple Linear Regression) models in determining the complex relationship between discharge and suspended sediment load.