Prediction of discharge in converging and diverging compound channel by gene expression programming

Abstract

ABSTRACT Discharge estimation in rivers is the most important parameter in flood management. Predicting discharge in the converging and diverging compound open channel by analytical approach leads to solving a system of complex nonlinear equations. In this study, the gene expression programming (GEP) is used for modeling and predicting of flow discharge in the non-prismatic compound open channel. Existing approaches on discharge estimation are surveyed in order to carry out a comparison among them and the proposed GEP model. Comparison of results showed that the divided channel method with vertical division lines with the coefficient of determination (0.74) and root-mean-square error (0.0093) is accurate among the analytical approaches. The non-dimensional parameters like friction factor ratio, area ratio, hydraulic radius ratio, bed slope, width ratio, relative flow depth, angle of converging or diverging, relative longitudinal distance, and flow aspect ratio have been taken as input parameters for predicting discharge. Gamma test has been performed and provided the best combination of input parameters as friction factor ratio, hydraulic radius ratio, relative flow depth, and bed slope which influence the discharge significantly. Results demonstrate that the conveyance capacity computed by the GEP model gives far better outcomes than the customary models. The GEP model with a coefficient of determination greater than 0.80 and mean absolute percentage error less than 15% for the testing stage has a suitable performance for predicting the discharge in the non-prismatic compound open channel.