scholarly journals Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming

2020 ◽  
pp. 202-217
Author(s):  
Hossein Bonakdari ◽  
Isa Ebtehaj ◽  
Bahram Gharabaghi ◽  
Ali Sharifi ◽  
Amir Mosavi
Keyword(s):  
Gene Expression ◽  
Discharge Capacity ◽  
Gene Expression Programming ◽  
Labyrinth Weir

scholarly journals Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming

2020 ◽  
Author(s):  
Hossein Bonakdari ◽  
Isa Ebtehaj ◽  
Bahram Gharabaghi ◽  
Ali Sharifi ◽  
Amir Mosavi
Keyword(s):  
Gene Expression ◽  
Relative Error ◽  
Discharge Coefficient ◽  
Gene Expression Programming ◽  
Vertex Angle ◽  
Dimensionless Parameters ◽  
Labyrinth Weir ◽  
Predicted Values ◽  
Crest Length ◽  
Labyrinth Weirs

This paper proposes a model based on gene expression programming for predicting discharge coefficient of triangular labyrinth weirs. The parameters influencing discharge coefficient prediction were first examined and presented as crest height ratio to the head over the crest of the weir (p/y), crest length of water to channel width (L/W), crest length of water to the head over the crest of the weir (L/y), Froude number (F=V/√(gy)) and vertex angle () dimensionless parameters. Different models were then presented using sensitivity analysis in order to examine each of the dimensionless parameters presented in this study. In addition, an equation was presented through the use of nonlinear regression (NLR) for the purpose of comparison with GEP. The results of the studies conducted by using different statistical indexes indicated that GEP is more capable than NLR. This is to the extent that GEP predicts the discharge coefficient with an average relative error of approximately 2.5% in such a manner that the predicted values have less than 5% relative error in the worst model.

Gene expression programming to predict Manning’s n in meandering flows

2018 ◽  
Vol 45 (4) ◽  
pp. 304-313 ◽  
Author(s):  
Arpan Pradhan ◽  
Kishanjit K. Khatua
Keyword(s):  
Gene Expression ◽  
Field Study ◽  
Discharge Capacity ◽  
Gene Expression Programming ◽  
Roughness Coefficient ◽  
Compound Channel ◽  
Factors Affecting ◽  
Manning’S N ◽  
Bed Material ◽  
Manning's N

Accurate prediction of Manning’s roughness coefficient is essential for the computation of conveyance capacity in open channels. There are various factors affecting the roughness coefficient in a meandering compound channel and not just the bed material. The factors, geometric as well as hydraulic, are investigated and incorporated in the prediction of Manning’s n. In this study, a new and accurate technique, gene expression programming (GEP) is used to estimate Manning’s n. The estimated value of Manning’s n is used in the evaluation of the conveyance capacity of meandering compound channels. Existing methods on conveyance estimation are assessed to carry out a comparison between them and the proposed GEP model. Results show that the discharge capacity computed by the new model provides far better results than the traditional models. The developed GEP model is validated with three individual sections of a natural river, signifying that the model can be applied to field study of rivers, within the stated range of parameters.

Gene Expression Programming Evolution Difficulty Prediction Based on Posture Model

2011 ◽  
Vol 22 (5) ◽  
pp. 899-913 ◽  
Author(s):  
Jiao-Ling ZHENG ◽  
Chang-Jie TANG ◽  
Kai-Kuo XU ◽  
Ning YANG ◽  
Lei DUAN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming

Modeling Self-reported Instrument Data with Gene Expression Programming

2013 ◽  
Vol 45 (6) ◽  
pp. 704-714
Author(s):  
Jinxin QIAN ◽  
Jiayuan YU
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming

Gene expression programming algorithm based on multi-threading evaluator

2013 ◽  
Vol 32 (4) ◽  
pp. 986-989
Author(s):  
Sheng-qiao NI ◽  
Chang-jie TANG ◽  
Ning YANG ◽  
Jie ZUO
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Programming Algorithm
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