Table 9 provides the statistical indicators of the employed kernel-depend models during the testing phase. It can be seen that under free-flow conditions, using the ratio of the downstream flow depth to the gate opening (y₃/w) as the only input parameter provided the desired prediction accuracy while using only the ratio of the downstream flow depth to the radial gate radius (y₃/r) as input did not yield the appropriate prediction accuracy. This can show the importance of the gate opening on the prediction process of the discharge coefficient under free-flow conditions. According to SI values, when comparing F(II) and F(III), adding the ratio of the gate opening to the radial gate radius (w/r) improves the average prediction accuracy of employed KELM-GWO and GPR approaches by 4 and 7%, respectively. It can be seen that the parameter y₁/w is the most influential parameter for the prediction of the discharge coefficient of radial gates under free-flow conditions. Taking into consideration the statistical indices, KELM-GWO-Polynomial and GPR-Exponential models generated the most accurate results with model F(IV), where y₁/w, w/r, and α-w/r were utilized as the input parameters. As mentioned in the previous section, under free-flow conditions, the comparison results of the employed kernel-depend methods revealed that the hybridized KELM-GWO-Polynomial with model F(IV) as input combination outperformed the SVM and GPR standalone models with R = 0.927, SI = 0.023, BIAS = −0.005946, and RMSE = 0.018. It should be noticed that the prediction of discharge coefficient under field conditions for multiple gates with different openings is complicated. It may become more intractable if one gate is free-flowing and another in the transition zone.