Gerger R.Kisi O.Dursun O.F.Emiroglu M.E.2024-08-042024-08-0420170733-9437https://doi.org/10.1061/(ASCE)IR.1943-4774.0001153https://hdl.handle.net/11616/90692The present study investigates the accuracy of five different data-driven techniques in estimating oxygen transfer efficiency in baffled chutes: feedforward neural network (FFNN), radial basis neural network (RBNN), generalized regression neural network (GRNN), adaptive neuro fuzzy inference system with subtractive clustering (ANFIS-SC), and adaptive neuro fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM). Baffled apron chutes or drops are used on channel structures to dissipate the energy in the flow. A baffled chute design is effective both in energy dissipation and in aerating the flow and reducing nitrogen supersaturation. There is a close relationship between energy dissipation and oxygen transfer efficiency. This study aims to determine the aeration efficiency of baffled chutes with stepped (S), wedge (W), trapezoidal (T), and T-shaped (T-S) baffle blocks. The performances of the FFNN, RBNN, GRNN, ANFIS-SC, and ANFIS-FCM models are compared with those of multilinear and nonlinear regression models. Based on the comparisons, it was observed that all data-driven models could be successfully employed in modeling the aeration efficiency of S, W, and T-S baffle blocks from the available experimental data. Among data-driven models, the FFNN model was found to be the best. © 2016 American Society of Civil Engineers.eninfo:eu-repo/semantics/closedAccessAeration efficiencyBaffled chuteData-driven modelingDissolved oxygenEnergy dissipationEnvironmental hydraulicsOxygen transferArticle143510.1061/(ASCE)IR.1943-4774.00011532-s2.0-85016809358Q2