Comparison of Neuro Fuzzy, Neural Network Artificial and Statistical Methods for Estimating Suspended Load Rivers (Case Study: Taleghan Basin Upstream)

Estimation of fine suspended load rivers is important in designing reserves, transition volume of sediment, and estimating lake pollution. Thus, some methods are needed for determining damages caused by sedimentations in environment and determining its effects on the watersheds. There are many methods for estimating suspended load, one of these methods that solves the problems of sediment discharge and can predict it is using Neuro fuzzy or ANFIS (Adaptive Network Fuzzy Inference System), and ANN (Artificial Neural Network) methods. These make a function between sediment and simultaneous discharge by use of different algorithms. The goal of this research is comparing the effectiveness of Neuro fuzzy, neural network artificial and statistical methods for estimating suspended load river in Glinak station of Taleghan Basin. It was found out that suspended load estimations of Nero fuzzy method with MAE 1006 ton/day, and correlation efficiency (R) 77%, RMSE 2621 ton/day and Nash-Sutcliff error (NS) 0.51 is better than Neural Network Artificial and Statistical methods and Artificial Neural Network method rather than Statistical Method are more proper. Also, contracting both neural networks artificial to fuzzy laws can be illustrated better than other methods, variation of sediment Load River. One more merit of this method is that it is not sensitive to few errors in early statistical data and this fact enables better estimation of neural network model in comparison with statistical model. Finally, Neuro fuzzy method works better as the percent of train data to test data increases.