Estimation of Suspended Sediment Yield in Natural Rivers Using Machine-coded Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Guven:2011:WRM,
  author =       "Aytac Guven and Ozgur Kisi",
  title =        "Estimation of Suspended Sediment Yield in Natural
                 Rivers Using Machine-coded Linear Genetic Programming",
  journal =      "Water Resources Management",
  year =         "2011",
  volume =       "25",
  number =       "2",
  pages =        "691--704",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Suspended sediment yield,
                 Modelling, Linear genetic programming, ANN, Neural
                 networks",
  publisher =    "Springer",
  ISSN =         "0920-4741",
  DOI =          "doi:10.1007/s11269-010-9721-x",
  size =         "14 pages",
  abstract =     "Estimation of suspended sediment yield is subject to
                 uncertainty and bias. Many methods have been developed
                 for estimating sediment yield but they still lack
                 accuracy and robustness. This paper investigates the
                 use of a machine-coded linear genetic programming (LGP)
                 in daily suspended sediment estimation. The accuracy of
                 LGP is compared with those of the Gene-expression
                 programming (GEP), which is another branch of GP, and
                 artificial neural network (ANN) technique. Daily
                 streamflow and suspended sediment data from two
                 stations on the Tongue River in Montana, USA, are used
                 as case studies. Root mean square error (RMSE) and
                 determination coefficient (R2) statistics are used for
                 evaluating the accuracy of the models. Based on the
                 comparison of the results, it is found that the LGP
                 performs better than the GEP and ANN techniques. The
                 GEP was also found to be better than the ANN. For the
                 upstream and downstream stations, it is found that the
                 LGP models with RMSE = 175 ton/day, R2 = 0.941 and RMSE
                 = 254 ton/day, R2 = 0.959 in test period is superior in
                 estimating daily suspended sediments than the best
                 accurate GEP model with RMSE = 231 ton/day, R2 = 0.941
                 and RMSE = 331 ton/day, R2 = 0.934, respectively.",
  affiliation =  "Civil Engineering Department, Hydraulics Division,
                 Gaziantep University, 27310 Gaziantep, Turkey",
}

Genetic Programming entries for Aytac Guven Ozgur Kisi

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