Determining Discharge Capacity of Converging Ogee spillways Using Support Vector Machine

Abstract Converging ogee spillways are employed to help mitigate the natural hazards, e.g. coincidental floods. However, due to complicated nature of converging spillway hydraulics; determining governing relationships is difficult and the outcomes of the accessible conventional models is not accurate. This study concentrates on the ability of Support Vector Machine (SVM) as a Meta-model technique for estimating discharging capacity of converging ogee spillways. In this regard, two different experimental datasets were applied for simulating. To consider the models developed, two scenarios with various input combinations were dealt while scenario1 consist of two parts. Scenario1a considers only free flow condition, scenario1b considers just submerged flow properties, and scenario2 considers general flow condition including free and submerged flow dataset. The obtained results confirmed that SVM is capable of estimating the discharge capacity. It was found that for prediction discharge capacity, the SVMs performed much more accurate in free flow stage (Scenario1a) in comparison with other situations. Sensitivity analysis indicated that the ratio of the entire upstream head to the height of spillway (???? / ????) and downstream apron position (????+ℎ???? / ???? ) under free flow condition, spillway geometry parameter (????2/????′.????????ℎ) on submerged flow condition, and the ratio of the entire upstream head to design head (???? / ????????) and ????2 / ????′.????????ℎ in general flow condition plays a key role in simulating process. Comparisons among SVMs and existing relationships demonstrated that SVM models produced better outcomes.