Water Science and Engineering 2018, 11(2) 167-176 DOI:   https://doi.org/10.1016/j.wse.2018.07.001  ISSN: 1674-2370 CN: 32-1785/TV

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Keywords
Circular channel
Gene expression programming (GEP)
Sensitivity analysis
Shear stress distribution
Soft computing
Authors
Zohreh Sheikh Khozani
Hossein Bonakdari
Isa Ebtehaj
PubMed
Article by Zohreh Sheikh Khozani
Article by Hossein Bonakdari
Article by Isa Ebtehaj

An expert system for predicting shear stress distribution in circular open channels using gene expression programming

Zohreh Sheikh Khozani, Hossein Bonakdari*, Isa Ebtehaj

Department of Civil Engineering, Razi University, Kermanshah 67131, Iran

Abstract

The shear stress distribution in circular channels was modeled in this study using gene expression programming (GEP). 173 sets of reliable data were collected under four flow conditions for use in the training and testing stages. The effect of input variables on GEP modeling was studied and 15 different GEP models with individual, binary, ternary, and quaternary input combinations were investigated. The sensitivity analysis results demonstrate that dimensionless parameter y/P, where y is the transverse coordinate, and P is the wetted perimeter, is the most influential parameter with regard to the shear stress distribution in circular channels. GEP model 10, with the parameter y/P and Reynolds number (Re) as inputs, outperformed the other GEP models, with a coefficient of determination of 0.7814 for the testing data set. An equation was derived from the best GEP model and its results were compared with an artificial neural network (ANN) model and an equation based on the Shannon entropy proposed by other researchers. The GEP model, with an average RMSE of 0.0301, exhibits superior performance over the Shannon entropy-based equation, with an average RMSE of 0.1049, and the ANN model, with an average RMSE of 0.2815 for all flow depths.

Keywords Circular channel   Gene expression programming (GEP)   Sensitivity analysis   Shear stress distribution   Soft computing  
Received 2017-01-31 Revised 2017-07-31 Online: 2018-04-30 
DOI: https://doi.org/10.1016/j.wse.2018.07.001
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Corresponding Authors: Hossein Bonakdari
Email: bonakdari@yahoo.com
About author:

References:

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