Comparison of genetic programming and radial basis function neural network for open-channel junction velocity field prediction

Abstract

The ability to accurately predict the velocity field in open-channel junctions has a significant impact on the designing process of many hydraulic structures, such as irrigation and drainage channels and sewer networks. The gene expression programming (GEP) and radial basis function neural network (RBF-NN) methods are developed in order to find a continuous spatial velocity description using discrete experimental measurements. By using the coordinates of each point and the tributary-to-main-discharge ratio of an open-channel junction, seven different input combinations are investigated. To find the optimum GEP model, various mathematical and linking functions are studied. The RBF-NN models are developed with various numbers of hidden layer nodes and amounts of spread. A comparison of the results indicates that both GEP and RBF-NN methods can accurately simulate flow in junctions. However, the GEP with root-mean-squared error (RMSE) of 0.2361 performs better than RBF-NN with RMSE of 0.2590. Owing to the higher accuracy and explicit output equation of the GEP, this method can be used in practical situations of predicting open-channel characteristics.