Rediscovering Manning's Equation Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Gaitan:2014:HIC,
  author =       "Carlos F. Gaitan",
  title =        "Rediscovering {Manning's} Equation Using Genetic
                 Programming",
  booktitle =    "11th International Conference on Hydroinformatics",
  year =         "2014",
  pages =        "Paper 323",
  address =      "New York, USA",
  month =        aug # " 17-21",
  organisation = "IAHR/IWA Joint Committee on Hydroinformatics",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-0-692-28129-1",
  URL =          "http://academicworks.cuny.edu/cc_conf_hic/323/",
  URL =          "http://academicworks.cuny.edu/cgi/viewcontent.cgi?article=1322&context=cc_conf_hic.pdf",
  broken =       "http://www.hic2014.org/proceedings/bitstream/handle/123456789/1549/1580.pdf",
  size =         "8 pages",
  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.",
  notes =        "http://www.hic2014.org/xmlui/",
}

Genetic Programming entries for Carlos F Gaitan

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