Sediment load prediction by genetic algorithms

Abstract

Accurate sediment load prediction is very important in planning, designing, operating and maintenance of water resources structures. Various models have been developed so far to identify the relation between discharge and sediment load. Most of the models based on regression method (RM) have some restrictive assumptions. This method is able to provide only one solution point for estimation of sediment amount. On the other hand, genetic algorithms (GAs) can produce more than one solution points providing optimal relation between discharge and sediment loads. Sediment load can be successfully predicted from discharge measurements by using GAs. Graphical and numerical data are presented to compare GAs with RM. GA methodology is applied to discharge and sediment load data obtained from Mississippi river at Missouri, St. Louis. It is found that GAs outperform RM in terms of mean relative error (MRE).