Investigation of Possibility of Suspended Sediment Prediction Using a Combination of Sediment Rating Curve and Artificial Neural Network Case Study: Ghatorchai River, Yazdakan Bridge

Estimation of sediment loads in rivers is one of the most important, difficult components of sediment transport studies and river engineering. Accessing new methods that can be effective in this background are more important. In this research, we have used the artificial neural network (ANN) to optimize the results of the sediment rating curve (SRC) to predict the suspended sediment loads. For doing that, the Yadakan station on Ghatoor-Chai River was considered. An equation by SRC method was obtained followed by an ANN method by the same data, and finally by combining them, we built a new model. It should be mentioned that before using the combined model, each method was used and the obtained results were compared with the observed data. Based on this research, the results of using the combined model were more precise than the ANN and SRC separately as the Dr value from 1. 402 (in SRC) and -2. 395 (in ANN) changed to 0. 963 in the combined model. The RMSE has also obtained 692.286 and 616.96 for SRC and ANN, respectively, whereas this value decreased to 603.094 for the combined model.