Chaos-based multigene genetic programming: A new hybrid strategy for river flow forecasting

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@Article{GHORBANI2018455,
  author =       "Mohammad Ali Ghorbani and Rahman Khatibi and 
                 Ali {Danandeh Mehr} and Hakimeh Asadi",
  title =        "Chaos-based multigene genetic programming: A new
                 hybrid strategy for river flow forecasting",
  journal =      "Journal of Hydrology",
  year =         "2018",
  volume =       "562",
  pages =        "455--467",
  keywords =     "genetic algorithms, genetic programming, Multigene
                 genetic programming (MGGP), Chaos theory, Forecasting,
                 Hybrid models, Phase-Space Reconstruction (PSR), River
                 flow",
  ISSN =         "0022-1694",
  URL =          "http://www.sciencedirect.com/science/article/pii/S002216941830307X",
  DOI =          "doi:10.1016/j.jhydrol.2018.04.054",
  abstract =     "Chaos theory is integrated with Multi-Gene Genetic
                 Programming (MGGP) engine as a new hybrid model for
                 river flow forecasting. This is to be referred to as
                 Chaos-MGGP and its performance is tested using daily
                 historic flow time series at four gauging stations in
                 two countries with a mix of both intermittent and
                 perennial rivers. Three models are developed: (i) Local
                 Prediction Model (LPM); (ii) standalone MGGP; and (iii)
                 Chaos-MGGP, where the first two models serve as the
                 benchmark for comparison purposes. The Phase-Space
                 Reconstruction (PSR) parameters of delay time and
                 embedding dimension form the dominant input signals
                 derived from original time series using chaos theory
                 and these are transferred to Chaos-MGGP. The paper
                 develops a procedure to identify global optimum values
                 of the PSR parameters for the construction of a
                 regression-type prediction model to implement the
                 Chaos-MGGP model. The inter-comparison of the results
                 at the selected four gauging stations shows that the
                 Chaos-MGGP model provides more accurate forecasts than
                 those of stand-alone MGGP or LPM models.",
}

Genetic Programming entries for Mohammad Ali Ghorbani Rahman Khatibi Ali Danandeh Mehr H Asadi

Citations