Article: ANFIS-based PCA to predict the longitudinal dispersion coefficient in rivers Journal: International Journal of Hydrology Science and Technology (IJHST) 2018 Vol.8 No.4 pp.410 - 424 Abstract: Study on the river water quality is the main part of environmental engineering. Longitudinal dispersion coefficient (DL) is one of the main important parameters in the river water quality studies. The DL is proportional to the flow velocity, channel width, depth of flow, and shear velocity. In this study the most effective parameters on DL were derived using principal component analysis (PCA). The results of PCA indicate that the width of the river, flow depth, and flow velocity are the most important parameters on the DL. In this paper with aim of the PCA results, the adaptive neuro fuzzy inference system (ANFIS) model was developed to predict the DL. The performance of developed ANFIS model based on PCA with error indices (R2 = 0.95 and RSME = 0.055) is suitable to predict the DL. During the development of ANFIS model, five neurons to the width channel, four neurons to the flow depth, four neurons to the velocity and two neurons to the shear velocity were assigned. The hyperbolic tangent sigmoid (tansig) function was considered as membership function for the neurons. Inderscience Publishers - linking academia, business and industry through research

Title: ANFIS-based PCA to predict the longitudinal dispersion coefficient in rivers

Authors: Abbas Parsaie; Samad Emamgholizadeh; Hazi Mohammad Azamathulla; Amir Hamzeh Haghiabi

Addresses: Department of Water Engineering, Lorestan University, Khorramabad, Iran ' Department of Water and Soil Engineering, Shahrood University of Technology, Shahrood, Semnan Province, Iran ' Faculty of Engineering University of Tabuk, Tabuk, 50060 Saudi Arabia ' Department of Water Engineering, Lorestan University, Khorramabad, Iran

Abstract: Study on the river water quality is the main part of environmental engineering. Longitudinal dispersion coefficient (D_L) is one of the main important parameters in the river water quality studies. The D_L is proportional to the flow velocity, channel width, depth of flow, and shear velocity. In this study the most effective parameters on D_L were derived using principal component analysis (PCA). The results of PCA indicate that the width of the river, flow depth, and flow velocity are the most important parameters on the D_L. In this paper with aim of the PCA results, the adaptive neuro fuzzy inference system (ANFIS) model was developed to predict the D_L. The performance of developed ANFIS model based on PCA with error indices (R² = 0.95 and RSME = 0.055) is suitable to predict the D_L. During the development of ANFIS model, five neurons to the width channel, four neurons to the flow depth, four neurons to the velocity and two neurons to the shear velocity were assigned. The hyperbolic tangent sigmoid (tansig) function was considered as membership function for the neurons.

Keywords: river mixing; water quality; river pollution; adaptive neuro fuzzy inference system; ANFIS; principal component analysis; PCA.

DOI: 10.1504/IJHST.2018.095537

International Journal of Hydrology Science and Technology, 2018 Vol.8 No.4, pp.410 - 424

Received: 29 Mar 2017
Accepted: 11 May 2017
Published online: 09 Oct 2018 *

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Title: ANFIS-based PCA to predict the longitudinal dispersion coefficient in rivers

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