Prediction Suspended Sediment Load of River Using Meta-heuristic Algorithms

In many areas of Iran, there is no detailed information on the amount of erosion, sediment transport and sedimentation of rivers, and in many cases, there are many difference between measurements. Due to the fact that the flow regime and consequently the sediment regime in the watersheds are not constant, the prediction of sediment rate helps to estimate the sediment accumulated behind the structures, specially the dams, and determine the dead volume of reservoirs in the future months, and by adopting timely arrangements facilitate the deposition management to a certain extent. In this research, three optimization algorithms including Genetic Algorithm (GA), Gray Wolf Optimizer (GWO) and Election Algorithm (EA) were used to predict the suspended sediment load of the rivers. In order to evaluate the performance of the algorithms, three statistics consists of R2, RMSE and NSE were used. The suspended sediment load of sedimentary station located in the Zarrineh-Rood river during the 2005-2015 are used as a case study. The results show GWO algorithm with values R2=0.96, RMSE=0.022 and NSE=0.74 has a very high accuracy compared to other algorithms used.