Linear genetic programming application for successive-station monthly streamflow prediction

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

@Article{DanandehMehr:2014:CG,
  author =       "Ali Danandeh Mehr and Ercan Kahya and Cahit Yerdelen",
  title =        "Linear genetic programming application for
                 successive-station monthly streamflow prediction",
  journal =      "Computer \& Geosciences",
  volume =       "70",
  pages =        "63--72",
  year =         "2014",
  ISSN =         "0098-3004",
  DOI =          "doi:10.1016/j.cageo.2014.04.015",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0098300414001010",
  abstract =     "In recent decades, artificial intelligence (AI)
                 techniques have been pronounced as a branch of computer
                 science to model wide range of hydrological phenomena.
                 A number of researches have been still comparing these
                 techniques in order to find more effective approaches
                 in terms of accuracy and applicability. In this study,
                 we examined the ability of linear genetic programming
                 (LGP) technique to model successive-station monthly
                 streamflow process, as an applied alternative for
                 streamflow prediction. A comparative efficiency study
                 between LGP and three different artificial neural
                 network algorithms, namely feed forward back
                 propagation (FFBP), generalised regression neural
                 networks (GRNN), and radial basis function (RBF), has
                 also been presented in this study. For this aim,
                 firstly, we put forward six different
                 successive-station monthly streamflow prediction
                 scenarios subjected to training by LGP and FFBP using
                 the field data recorded at two gauging stations on
                 Coruh River, Turkey. Based on Nash-Sutcliffe and root
                 mean squared error measures, we then compared the
                 efficiency of these techniques and selected the best
                 prediction scenario. Eventually, GRNN and RBF
                 algorithms were used to restructure the selected
                 scenario and to compare with corresponding FFBP and
                 LGP. Our results indicated the promising role of LGP
                 for successive-station monthly streamflow prediction
                 providing more accurate results than those of all the
                 ANN algorithms. We found an explicit LGP-based
                 expression evolved by only the basic arithmetic
                 functions as the best prediction model for the river,
                 which uses the records of the both target and upstream
                 stations.",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 neural networks, Streamflow prediction, Successive
                 stations",
}

Genetic Programming entries for Ali Danandeh Mehr Ercan Kahya Cahit Yerdelen

Citations