Applicability of Several Soft Computing Approaches in Modeling Oxygen Transfer Efficiency at Baffled Chutes
JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING, cilt.143, sa.5, 2017 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 143 Sayı: 5
- Basım Tarihi: 2017
- Doi Numarası: 10.1061/(asce)ir.1943-4774.0001153
- Dergi Adı: JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Anahtar Kelimeler: 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
- İnönü Üniversitesi Adresli: Evet
Özet
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.