Application of AI Approaches to Estimate Discharge Coefficient of Novel Kind of Sharp-Crested V-Notch Weirs

In this study, the hydraulic features of the $SCVW$ (a novel type of sharp-crested V-notch weirs) were scrutinized in the popular vertex angles $\theta$, i.e., 30°, 45°, 60°, 90°, 120°, 128°, and 150°, under aerated, steady and free overflow conditions in an open channel for large physical models. To assess the changes of the discharge coefficient of the $SCVW$ (i.e., $C_{\mathit{dSCVW}}$) versus weir height and $\theta$, widespread laboratory works were performed by measuring the water head over the crest of the weir and the discharge. Special formulas for the $C_{dSCVW}$ in the $\theta =60°$ were checked, and an appropriate empirical equation was recommended. The calculated $C_{dSCVW}$ by the proposed equation was within 0%–10% of the measured values. Three types of nonparametric artificial intelligence (AI) methods, namely, support vector regression ($SVR$), gene expression programming ($GEP$), and a robust hybrid model entitled hybrid ($SVR\text{-}ACO$) were developed to estimate the $C_{\mathit{dSCVW}}$. For the sake of modeling, 196 experimental datasets were applied in the mentioned methods to evaluate the $C_{dSCVW}$ by taking into consideration the dimensionless variables which impact the determining procedure of the $C_{dSCVW}$. In this modeling, by varying the architecture and core factors of the aforementioned methods, several scenarios were defined. The generated mathematical equation of $C_{dSCVW}$ by the best scenario of the $GEP$ was compared with the corresponding measured values in which the results were in 0%–10%. According to the attained statistical indices, scatter plots, and the values of the total grade ($TG$) technique, the hybrid $SVR(RBF)\text{-}ACO$ model was determined as the superior and optimal method to estimate the $C_{dSCVW}$ with high performance and accuracy.