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 degrees, 45 degrees, 60 degrees, 90 degrees, 120 degrees, 128 degrees, and 150 degrees, 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-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 degrees 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-ACO) were developed to estimate the C-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