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In this study, the SCVW (a novel type of Sharp-Crested V-notch weirs) is employed for more experimentally and theoretically investigations. The length of the hydraulic jump at downstream of the SCVW (L jSCVW ) is measured via new supplementary experimental datasets via most popular vertex angles θ (128° and 60°). The experiments are conducted under aerated, steady, and free overflow conditions in an open channel for large physical models. To assess the variations of L jSCVW  versus the θ, extensive laboratory work is performed at different discharges (Q), diverse triangular segments number (N st ) in the SCVW, and various tailgate angles (ɸ). Via dimensional analysis and pre-processing method, most effectual parameters on the L jSCVW  were obtained as Fr 1  (approaching Froude number), Re 1  (incoming Reynolds number), and(relative sequent depths). Based on the experimental results, by increasing the values of Fr 1  and Re 1 , the values of L jSCVW  noticeably increased. Three types of data-driven models (DDMs), namely, support vector regression (SVR), gene expression programming (GEP), and a robust hybrid model entitled hybrid (SVR-ACO) are developed to estimate the L jSCVW . The suggested hybrid model is a coupled form of SVR with ant colony optimization (ACO) algorithm, which is used to enhance the estimation precision of the L jSCVW . According to the attained statistical indices (determination coefficient (R 2 ) = 0.98, root mean square error (RMSE) = 0.191, and value of total grade (TG) = 19.78) and scatter plots, the hybrid SVR-ACO model was determined as the most superior method to estimate the L jSCVW  with high accuracy.

Using a hybrid artificial intelligence approach to estimate length of the hydraulic jump caused by novel kind of Sharp-Crested V-notch weirs