Modeling of streamflow- suspended sediment load relationship by adaptive neuro-fuzzy and artificial neural network approaches (Case study: Dalaki River, Iran)

Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to its essential application to water resources management. Recently, artificial intelligence has gained much popularity owing to its application in calibrating the nonlinear relationships inherent in the stream flow suspended sediment relationship. This study made us of adaptive neuro-fuzzy inference system (ANFIS) techniques and three artificial neural network approaches, namely, the Feed-forward back-propagation (FFBP), radial basis function based neural networks (RBF), geomorphology-based artificial neural network (GANN) to predict the streamflow suspended sediment relationship. To illustrate their applicability and efficiency,, the daily streamflow and suspended sediment data of Dalaki River station in south of Iran were used as a case study. The obtained results were compared with the sediment rating curve (SRC) and regression model (RM). Statistic measures (RMSE, MAE, and R2) were used to evaluate the performance of the models. From the results, adaptive neuro-fuzzy (ANFIS) approach combined capabilities of both Artificial Neural Networks and Fuzzy Logic and then reflected more accurate predictions of the system. The results showed that accuracy of estimations provided by ANFIS was higher than ANN approaches, regression model and sediment rating curve. Additionally, relating selected geomorphologic parameters as the inputs of the ANN with rainfall depth and peak runoff rate enhanced the accuracy of runoff rate, while sediment loss predictions from the watershed and GANN model performed better than the other ANN approaches together witj regression equations in Modeling of stream flow suspended sediment relationship.