An application of artificial intelligence for rainfall-runoff modeling

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

@Article{Aytek:2008:JESS,
  author =       "Ali Aytek and M Asce and Murat Alp",
  title =        "An application of artificial intelligence for
                 rainfall-runoff modeling",
  journal =      "Journal of Earth System Science",
  year =         "2008",
  volume =       "117",
  number =       "2",
  pages =        "145--155",
  month =        apr,
  email =        "aytek@gantep.edu.tr",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  URL =          "http://www.ias.ac.in/jess/apr2008/d093.pdf",
  size =         "11 pages",
  abstract =     "This study proposes an application of two techniques
                 of artificial intelligence (AI) for rainfall-runoff
                 modelling: the artificial neural networks (ANN) and the
                 evolutionary computation (EC). Two different ANN
                 techniques, the feed forward back propagation (FFBP)
                 and generalised regression neural network (GRNN)
                 methods are compared with one EC method, Gene
                 Expression Programming (GEP) which is a new
                 evolutionary algorithm that evolves computer programs.
                 The daily hydrometeorological data of three rainfall
                 stations and one streamflow station for Juniata River
                 Basin in Pennsylvania state of USA are taken into
                 consideration in the model development. Statistical
                 parameters such as average, standard deviation,
                 coefficient of variation, skewness, minimum and maximum
                 values, as well as criteria such as mean square error
                 (MSE) and determination coefficient (R2) are used to
                 measure the performance of the models. The results
                 indicate that the proposed genetic programming (GP)
                 formulation performs quite well compared to results
                 obtained by ANNs and is quite practical for use. It is
                 concluded from the results that GEP can be proposed as
                 an alternative to ANN models.",
}

Genetic Programming entries for Ali Aytek M Asce Murat Alp

Citations