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Experimental study and artificial intelligence-based modeling of discharge coefficient of converging ogee spillways

ABSTRACT Converging ogee spillway is employed as an emergency spillway constructed to control natural disasters such as instant floods. This paper presents an experimental study and artificial intelligence-based modeling of the discharge coefficient (Cd ) of the converging ogee spillway (with a curve axis). The ogee spillway structure was tested in both symmetrical and asymmetrical convergence of training walls ranging from 0° to 120°. Through the visual observation, it was found that the tailwater submergence, the depth of approach, the crest and spillway geometry, the head different from design head, and downstream floor position are the important factors that alter the Cd of the converging ogee spillway. Moreover, two different methods (e.g. gene expression programming (GEP) and adaptive neuro fuzzy inference systems (ANFIS)) were assessed for modeling the Cd using original experimental data. Based on the obtained results, both the GEP and ANFIS models have reliable performance in simulating the Cd . Moreover, the performance of GEP model was found to be slightly better than the ANFIS technique. According to the outcome of sensitivity analysis, the ratio of the total upstream head on the spillway to design head (H/Hd ) is the most important parameter for the optimum modeling of the Cd of the converging ogee spillway.