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

Abstract

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 θ, 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., CdSCVW) versus weir height and θ, widespread laboratory works were performed by measuring the water head over the crest of the weir and the discharge. Special formulas for the CdSCVW in the θ=60° were checked, and an appropriate empirical equation was recommended. The calculated CdSCVW 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 CdSCVW. For the sake of modeling, 196 experimental datasets were applied in the mentioned methods to evaluate the CdSCVW by taking into consideration the dimensionless variables which impact the determining procedure of the CdSCVW. In this modeling, by varying the architecture and core factors of the aforementioned methods, several scenarios were defined. The generated mathematical equation of CdSCVW 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)-ACO model was determined as the superior and optimal method to estimate the CdSCVW with high performance and accuracy.