Assessing the Capability of Adaptive Neuro Fuzzy Interference System (ANFIS) in Estimating the Amount of Suspended Sediment Load and its Comparison with Two Models of Artificial Neutral Fuzzy Inference System Case study: Zarine rood, South east basin of Urmia Lake

Load sediment transport in rivers is important according to their role in pollution, Reservoir filling, hydroelectric equipment life, Fish and other hydrological issues. Direct measurement of suspended sediment load in rivers is expensive and construction of measurement stations along the river is not possible. The equations used to estimate the sediment load are not applicable for all areas and also require long-term monitoring. In this study, to estimate daily sediment load, the Neural Fuzzy Inference System (ANFIS) is used. For this, daily discharge and suspended sediment load data of 365 days of years 2007 and 2009 of Zarine rood located in the south east of Urmia Lake is used for training and testing the Artificial Neutral Fuzzy Inference System. Southeast basin of Urmia Lake due to its hydrological and litologhical conditions have high rates of sediment production. ANFIS model is a nonlinear model and this is a great advantage. Note that the suspended sediment load also follows a linear relationship, so this model can achieve more accurate and more realistic results. This model of the multilayer Perceptron model (MLP), Neural networks, radial basis function (RBF), and sediment measures curve (SRC) has been used in these estimates. The results of ANFIS model is compared with the above models. To determine the model efficiency, the mean square error factor (RMSE) and explanation error (R2) was used and it can be seen that the ANFIS model achieves better results than the other models