Estimation of Suspended Sediment Load Values of the River Using Artificial Intelligence Methods (Case study of Maymeh River)

The use of appropriate methods for estimating sediment load has long been considered by experts in river problems. In this study, The suspended sediment load of the river was estimated using two artificial intelligence methods, including genetic programming (GP) based on graph and radial base functions (RBF) method. The implementations use data from 12-year statistics and information from four hydrometric stations of Gourab, Sarkmar, Dehloran, and Bayat roads on Meymeh River in Ilam province. In this study, the parameters of moon number and river flow were used as inlet parameters and river sediment load as outlet parameters. In the equations obtained from genetic programming method (GP), the highest correlation In the equations obtained from genetic programming (GP) method, the highest correlation obtained was related to Gorab hydrometric station with 99.18% and the lowest correlation was obtained related to the aggregated data of four hydrometric stations with 92.17%. In the radial baseline functions (RBF) method, the maximum correlation between educational and experimental data related to Bayat station was obtained with 100% and 94.20%, respectively, and the results. The results showed that radial basis functions (RBF) had better performance than genetic programming (GP) in estimating the suspended sediment load of Meymeh River