Suspended sediment load prediction of river systems: GEP approach

Abstract

This study presents gene expression programming (GEP), an extension of genetic programming, as an alternative approach to modeling the suspended sediment load relationship for the three Malaysian rivers. In this study, adaptive neuro-fuzzy inference system (ANFIS), regression model, and GEP approaches were developed to predict suspended load in three Malaysian rivers: Muda River, Langat River, and Kurau River [ANFIS (R 2 = 0.93, root mean square error (RMSE) = 3.19, and average error (AE) = 1.12) and regression model (R 2 = 0.63, RMSE = 13.96, and AE = 12.69)]. Additionally, the explicit formulations of the developed GEP models are presented (R 2 = 0.88, RMSE = 5.19, and AE = 6.5). The performance of the GEP model was found to be acceptable compare to ANFIS and better than the conventional models.