Bridge backwater estimation: A comparison between artificial intelligence models and explicit equations
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{Niazkar:2021:SCI,
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author = "Majid Niazkar and Nasser Talebbeydokhti and
Seied Hosein Afzali",
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title = "Bridge backwater estimation: A comparison between
artificial intelligence models and explicit equations",
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journal = "Scientia Iranica: Transactions on Civil Engineering
(A)",
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year = "2021",
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volume = "28",
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number = "2",
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pages = "573--585",
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month = mar # " and " # apr,
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keywords = "genetic algorithms, genetic programming, Hydraulic
structures, bridge backwater estimation, explicit
equation, artificial neural network, ANN",
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ISSN = "1026-3098",
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eissn = "2345-3605",
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publisher = "Sharif University of Technology",
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URL = "https://scientiairanica.sharif.edu/article_21738.html",
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eprint = "https://scientiairanica.sharif.edu/article_21738_e2c06ad90c2d537acd956ffe65ff1d46.pdf",
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DOI = "doi:10.24200/sci.2020.51432.2175",
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size = "13 pages",
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abstract = "Estimation of bridge backwater has been one of
practical challenges in hydraulic engineering for
decades. In this study, Genetic Programming (GP) has
been applied for estimating bridge backwater for the
first time based on the conducted literature review.
Furthermore, two new explicit equations are developed
for predicting bridge afflux using Genetic Algorithm
(GA) and hybrid MHBMO-GRG algorithm. The performances
of these models are compared with Artificial Neural
Network (ANN) and several explicit equations available
in the literature considering both laboratory and field
data. Based on five considered performance evaluation
criteria, the two new explicit equations outperform the
ones available in the literature. Furthermore, GP and
ANN achieve the best results in favor of four out of
five considered criteria for train and test data,
respectively. To be more specific, ANN improves the MSE
and R2 values of the explicit equation developed using
GA by 44 percent and 12 percent for the test data while
GP enhances the corresponding values by 62 percent and
9 percent for the train data. Finally, the results
demonstrate that not only artificial intelligence
models considerably improve bridge afflux estimation
than the explicit equations but also the suggested
equations significantly improve the accuracy of the
available explicit ones.",
-
notes = "School of Engineering, Department of Civil and
Environmental Engineering, Shiraz University, Zand
Blvd., Shiraz, Iran",
- }
Genetic Programming entries for
Majid Niazkar
Nasser Talebbeydokhti
Seied Hosein Afzali
Citations