Discharge modeling and characteristic analysis of semi-circular side weir based on the soft computing method

Abstract

Abstract In this study, a support vector machine (SVM) and three optimization algorithms are used to develop a discharge coefficient (Cd) prediction model for the semi-circular side weir (SCSW). After that, we derived the input and output parameters of the model by dimensionless analysis as the ratio of the flow depth at the weir crest point upstream to the diameter (h1/D), the ratio of main channel width to diameter (B/D), the ratio of side weir height to diameter (P/D), upstream of side weir Froude number (Fr), and Cd. The sensitivity coefficients for dimensionless parameters to Cd were calculated based on Sobol's method. The research shows that SVM and Genetic Algorithm (GA-SVM) have high prediction accuracy and generalization ability; the average error and maximum error were 0.08 and 2.47%, respectively, which were about 95.72 and 60.86% lower compared with the traditional empirical model. The first-order sensitivity coefficients S1 and global sensitivity coefficients Si of h1/D, B/D, P/D, and Fr were 0.35, 0.07, 0.13, and 0.02; 0.63, 0.25, 0.30, and 0.32, respectively. h1/D has a significant effect on Cd. In particular, when h1/D < 0.24 and 0.48 < Fr < 0.58, 0.67 < Fr < 0.72, the discharge capacity of the SCSW is relatively large.