Rediscovering Manning's Equation Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Gaitan:2014:HIC,
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author = "Carlos F. Gaitan",
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title = "Rediscovering {Manning's} Equation Using Genetic
Programming",
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booktitle = "11th International Conference on Hydroinformatics",
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year = "2014",
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pages = "Paper 323",
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address = "New York, USA",
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month = aug # " 17-21",
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organisation = "IAHR/IWA Joint Committee on Hydroinformatics",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-0-692-28129-1",
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URL = "http://academicworks.cuny.edu/cc_conf_hic/323/",
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URL = "http://academicworks.cuny.edu/cgi/viewcontent.cgi?article=1322&context=cc_conf_hic.pdf",
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broken = "http://www.hic2014.org/proceedings/bitstream/handle/123456789/1549/1580.pdf",
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size = "8 pages",
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abstract = "Open-channel hydraulics (OCH) research traditionally
links empirical formulae to observational data. One of
the most common equations in OCH is Manning's formula
for open channel flow (Q) driven by gravity (also known
as the Gauckler-Manning-Strickler formula). The formula
relates the cross-sectional average velocity (V=Q/A),
the hydraulic radius (R), and the slope of the water
surface (S) with a friction coefficient n,
characteristic of the channel's surface. Here we show a
practical example where Genetic Programming (GP), a
technique derived from Bioinformatics, can be used to
derive an empirical relationship based on different
synthetic datasets of the aforementioned parameters.
Specifically, we evaluated if Manning's formula could
be retrieved from datasets with 300 pentads of A, n, R,
S, and Q (from Mannings equation) using GP. The
cross-validated results show success retrieving the
functional form from the synthetic data and encourage
the application of GP on problems where traditional
empirical relationships show high biases, like sediment
transport. The results also show alternative flow
equations that can be used in the absence of one of the
predictors and approximate Manning equation.",
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notes = "Broken June 2021 http://www.hic2014.org/xmlui/",
- }
Genetic Programming entries for
Carlos F Gaitan
Citations