Soft Computing Application to Amplify Discharge Coefficient Prediction in Side Rectangular Weirs

It’s valuable to predict accurately the discharge coefficient due to its direct role in the determination of the weirs passing capacity. This study was carried out using intelligent GEP and SVM algorithms based on laboratory datasets to simulate the discharge coefficient of the rectangular side weir installed in a rectangular (the first scenario) and a trapezoidal main channel (the second scenario). The most effective parameters were determined as upstream Froud number (Fr1), upstream flow depth (h1 or yo), weir height (P or W), side weir length (L), main canal width (b), sidewall slope (Z). Dimensionless parameters were extracted as (Fr1, , , ) and (Fr1, Z, ) for the first and the second scenarios, respectively. The outputs of the two algorithms were compared with experimental and regression equations using statistical indices as root mean square error (RMSE), deterministic coefficient (R2), relative error (RE), and standardized developed discrepancy ratio (ZDDR). The values of (RMSE, R2, RE, ZDDR) during the test phase for the first scenario for GEP and SVM were calculated as (0.036, 0.962, 7.76, 5.48) and (0.037, 0.952, 9.6, 3.8) and those of the superior regression model were (0.040, 0.912, 4.527, 2.439), respectively. The corresponding values in the second scenario for GEP, SVM and regression model were obtained (0.068, 0.992, 3.1, 1.14), (0.043, 0.934, 10.3, 0.71) and (0.068, 0.818, 11.9, 0.511), respectively. The results showed the superiority of intelligent algorithm over classical regression, and also the GEP to the SVM.