Applicability of Several Soft Computing Approaches in Modeling Oxygen Transfer Efficiency at Baffled Chutes

Gerger R., Kisi O., Dursun Ö. F., Emiroglu M. E.

JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING, vol.143, no.5, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 143 Issue: 5
  • Publication Date: 2017
  • Doi Number: 10.1061/(asce)ir.1943-4774.0001153
  • Journal Name: JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: Aeration efficiency, Baffled chute, Data-driven modeling, Dissolved oxygen, Energy dissipation, Environmental hydraulics, Oxygen transfer, AIR ENTRAINMENT, ENERGY-DISSIPATION, AERATION EFFICIENCY, NEURO-FUZZY, PERFORMANCE, PREDICTION, NETWORKS, ANFIS, FLOWS, WEIRS
  • Inonu University Affiliated: Yes

Abstract

The 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. (C) 2017 American Society of Civil Engineers.