Genetic Programming Approach to Predict Sediment Concentration for Malaysian Rivers

Abstract

The assessment of the volume of sediment transported by a river is of vital interest in hydraulic engineering due to its importance in the design and river management. A total of 346 sets of bed load data obtained from literature. These assessments, based on the median sediment size (d50), show that the existing equations were unable to predict the measured bed load accurately. All existing equations over-predicted the measured values, and none of the existing bed load equations gave satisfactory performance when tested on local river data. Therefore, the present study applies a new soft computing technique, i.e. genetic programming (GP), to better predicts measured bed load data. Researchers have noticed that the use of soft computing techniques as alternative to conventional statistical methods based on controlled laboratory or field data, gave significantly better results. Gene-expression programming (GEP), which is an extension to genetic programming (GP), has nowadays attracted the attention of researchers in prediction of hydraulic data. The predictions of GEP models were observed to be in strictly good agreement of measured ones, and quite better than conventional GP and the regression-based formulae.