Review of Genetic Algorithm Model for Suspended Sediment Estimation

Number of attempts have been made to relate the amount of sediment transported by a river to flow conditions such as discharge, velocity and shear stress. However, none of the equations derived have received universal acceptance. Usually, either the weight or the concentration of sediment is related to the discharge. Fluxes of suspended materials collected at gauge stations are closely related to flow discharges. In the absence of continuous recorded suspended sediment concentration data, hydrologists have used different models such as rating sediment transport curves to define the water discharge, suspended sediment relationship and to estimate (predict) suspended sediment concentrations for use in flux calculations. Correct estimation of sediment volume carried by a river is very important for many water resources projects. Empirical relations such as sediment rating curves are often applied to determine the average relationship between discharge and suspended sediment load. This type of models generally underestimates or overestimates the amount of sediment. During recent decades, some black box models based on artificial neural networks have been developed to overcome this problem. GA has been applied to a wide range of problems in artificial intelligence, engineering and science applications, industrial, and mechanical models. The main purpose of this paper is literature review of Genetic Algorithm for suspended sediment estimation