Linear genetic programming for time-series modelling of daily flow rate

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Aytac Guven",
  title =        "Linear genetic programming for time-series modelling
                 of daily flow rate",
  journal =      "Journal of Earth System Science",
  year =         "2009",
  volume =       "118",
  number =       "2",
  pages =        "137--146",
  month =        apr,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, neural
                 networks, daily flows, flow forecasting",
  ISSN =         "0253-4126",
  URL =          "",
  size =         "10 pages",
  abstract =     "In this study linear genetic programming (LGP),which
                 is a variant of Genetic Programming,and two versions of
                 Neural Networks (NNs)are used in predicting time-series
                 of daily flow rates at a station on Schuylkill River at
                 Berne,PA,USA.Daily flow rate at present is being
                 predicted based on different time-series scenarios.For
                 this purpose,various LGP and NN models are calibrated
                 with training sets and validated by testing
                 sets.Additionally,the robustness of the proposed LGP
                 and NN models are evaluated by application data,which
                 are used neither in training nor at testing stage.The
                 results showed that both techniques predicted the flow
                 rate data in quite good agreement with the observed
                 ones,and the predictions of LGP and NN are
                 challenging.The performance of LGP,which was moderately
                 better than NN,is very promising and hence supports the
                 use of LGP in predicting of river flow data.",
  notes =        "Civil Engineering Department, Gaziantep University,
                 27310 Gaziantep, Turkey.",

Genetic Programming entries for Aytac Guven