Comparison of Suspended Sediment Estimation by Artificial Neural Network and Sediment Rating Curve Methods (Case Study: Doogh River in Golestan Province)

There is a lack of information about erosion, sediment transport and sedimentation in our country and usually there is a significant difference between computed and measured data. Due to this fact that the rivers always is under erosion, the study of sediment transport is very important in river hydraulic and geomorphology. Sediment transport phenomenon is one of the important processes which influence many of the hydraulic and river structures, and one of the biggest problems for using water recourses in the world. In this study, artificial neural network was used as an effective way in order to estimate suspended sediment load in Doogh river in Golestan province. The flow discharge in present day, past day and hydrograph situation were used as input parameters, while the suspended sediment load was used as output parameter. The MLP neural network with tangent sigmoid activation function was used for training the network. The results show that the artificial neural networks estimate the suspended sediment load more accurately (R2=0.98, RMSE=0.015, NASH=0.97) than available method such as rating curve method with and without data classification.