An expert system for predicting Manning's roughness coefficient in open channels by using gene expression programming

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@Article{journals/nca/AzamathullaAG13,
  title =        "An expert system for predicting Manning's roughness
                 coefficient in open channels by using gene expression
                 programming",
  author =       "H. Md. Azamathulla and Zulfequar Ahmad and 
                 Aminuddin {Ab. Ghani}",
  journal =      "Neural Computing and Applications",
  year =         "2013",
  number =       "5",
  volume =       "23",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, GEP",
  bibdate =      "2013-11-19",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/nca/nca23.html#AzamathullaAG13",
  pages =        "1343--1349",
  URL =          "http://dx.doi.org/10.1007/s00521-012-1078-z",
  size =         "7 pages",
  abstract =     "Manning's roughness coefficient (n) has been widely
                 used in the estimation of flood discharges or depths of
                 flow in natural channels. Accurate estimation of
                 Manning's roughness coefficient is essential for the
                 computation of flow rate, velocity. Conventional
                 formulae that are greatly based on empirical methods
                 lack in providing high accuracy for the prediction of
                 Manning's roughness coefficient. Consequently, new and
                 accurate techniques are still highly demanded. In this
                 study, gene expression programming (GEP) is used to
                 estimate the Manning roughness coefficient. The
                 estimated value of the roughness coefficient is used in
                 Mannings equation to compute the flow parameters in
                 open-channel flows in order to carry out a comparison
                 between the proposed GEP-based approach and the
                 conventional ones. Results show that computed discharge
                 using estimated value of roughness coefficient by GEP
                 is in good agreement (10percent) with the experimental
                 results compared to the conventional formulae
                 (R-squared = 0.97 and RMSE = 0.0034 for the training
                 data and Rsquared = 0.94 and RMSE = 0.086 for the
                 testing data).",
}

Genetic Programming entries for Hazi Mohammad Azamathulla Z Ahmad Aminuddin Ab Ghani

Citations