A genetic programming approach to suspended sediment modelling

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

@Article{Aytek:2008:JH,
  author =       "Ali Aytek and Ozgur Kisi",
  title =        "A genetic programming approach to suspended sediment
                 modelling",
  journal =      "Journal of Hydrology",
  year =         "2008",
  volume =       "351",
  number =       "3-4",
  pages =        "288--298",
  month =        "15 " # apr,
  keywords =     "genetic algorithms, genetic programming, Suspended
                 sediment load, Rating curves, Soft computing",
  DOI =          "doi:10.1016/j.jhydrol.2007.12.005",
  abstract =     "This study proposes genetic programming (GP) as a new
                 approach for the explicit formulation of daily
                 suspended sediment-discharge relationship. Empirical
                 relations such as sediment rating curves are often
                 applied to determine the average relationship between
                 discharge and suspended sediment load. This type of
                 models generally underestimates or overestimates the
                 amount of sediment. During recent decades, some black
                 box models based on artificial neural networks have
                 been developed to overcome this problem. But these type
                 of models are implicit that can not be simply used by
                 other investigators. Therefore it is still necessary to
                 develop an explicit model for the discharge-sediment
                 relationship. It is aimed in this study, to develop an
                 explicit model based on genetic programming. Explicit
                 models obtained using the GP are compared with rating
                 curves and multi-linear regression techniques in
                 suspended sediment load estimation. The daily
                 streamflow and suspended sediment data from two
                 stations on Tongue River in Montana are used as case
                 studies. The results indicate that the proposed GP
                 formulation performs quite well compared to sediment
                 rating curves and multi-linear regression models and is
                 quite practical for use.",
  notes =        "Gaziantep University, Civil Engineering Department,
                 Hydraulics Division, 27310 Gaziantep, Turkey

                 Erciyes University, Civil Engineering Department,
                 Hydraulics Division, 38039 Kayseri, Turkey",
}

Genetic Programming entries for Ali Aytek Ozgur Kisi

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