An improved gene expression programming model for streamflow forecasting in intermittent streams

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@Article{DANANDEHMEHR2018669,
  author =       "Ali {Danandeh Mehr}",
  title =        "An improved gene expression programming model for
                 streamflow forecasting in intermittent streams",
  journal =      "Journal of Hydrology",
  year =         "2018",
  volume =       "563",
  pages =        "669--678",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Gene
                 expression programming, Streamflow forecasting,
                 Evolutionary optimization, Intermittent streams",
  ISSN =         "0022-1694",
  URL =          "https://www.sciencedirect.com/science/article/pii/S0022169418304712",
  DOI =          "doi:10.1016/j.jhydrol.2018.06.049",
  abstract =     "Skilful forecasting of monthly streamflow in
                 intermittent rivers is a challenging task in stochastic
                 hydrology. In this study, genetic algorithm (GA) was
                 combined with gene expression programming (GEP) as a
                 new hybrid model for month ahead streamflow forecasting
                 in an intermittent stream. The hybrid model was named
                 GEP-GA in which sub-expression trees of the best
                 evolved GEP model were rescaled by appropriate
                 weighting coefficients through the use of GA optimizer.
                 Auto-correlation and partial auto-correlation functions
                 of the streamflow records as well as evolutionary
                 search of GEP were used to identify the optimum
                 predictors (i.e., number of lags) for the model. The
                 proposed methodology was demonstrated using monthly
                 streamflow data from the Shavir Creek in Iran.
                 Performance of the GEP-GA was compared to that of
                 classic genetic programming (GP), GEP, multiple linear
                 regression and GEP-linear regression models developed
                 in the present study as the benchmarks. The results
                 showed that the GEP-GA outperforms all the benchmarks
                 and motivated to be used in practice.",
}

Genetic Programming entries for Ali Danandeh Mehr

Citations