A genetic programming-based model for drag coefficient of emergent vegetation in open channel flows
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{LIU:2020:AWR,
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author = "Meng-Yang Liu and Wen-Xin Huai and Zhong-Hua Yang and
Yu-Hong Zeng",
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title = "A genetic programming-based model for drag coefficient
of emergent vegetation in open channel flows",
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journal = "Advances in Water Resources",
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volume = "140",
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pages = "103582",
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year = "2020",
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ISSN = "0309-1708",
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DOI = "doi:10.1016/j.advwatres.2020.103582",
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URL = "http://www.sciencedirect.com/science/article/pii/S0309170819302222",
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keywords = "genetic algorithms, genetic programming, Drag
coefficient, Vegetated flows, Estimating drag, Cylinder
arrays",
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abstract = "The estimation of drag exerted by vegetation is of
great interest because of its importance in assessing
the impact of vegetation on the hydrodynamic processes
in aquatic environments. In the current research,
genetic programming (GP), a machine learning (ML)
technique based on natural selection, was adopted to
search for a robust relationship between the bulk drag
coefficient (Cd) for arrays of rigid circular cylinders
representing emergent vegetation with blockage ratio
(ψ), vegetation density (lambda) and pore Reynolds
number (Rep) based on published data. We use a data set
covering a wide range of each parameter involved to
cover all possible dependencies. A new predictor, which
shares the same form with the Ergun-derived formula,
was obtained without any pre-specified forms before
searching. The dependence of the two parameters in
Ergun equation on vegetation characteristics was also
estimated by GP. This new Cd predictor for emergent
vegetation with a relatively concise form exhibits a
considerable improvement in terms of prediction ability
relative to existing predictors",
- }
Genetic Programming entries for
Meng-Yang Liu
Wen-Xin Huai
Zhong-Hua Yang
Yu-Hong Zeng
Citations