Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations

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@Article{Shiri2010,
  author =       "Jalal Shiri and Ozgur Kisi",
  title =        "Comparison of genetic programming with neuro-fuzzy
                 systems for predicting short-term water table depth
                 fluctuations",
  journal =      "Computer \& Geosciences",
  volume =       "37",
  number =       "10",
  pages =        "1692--1701",
  month =        oct,
  year =         "2011",
  ISSN =         "0098-3004",
  DOI =          "doi:10.1016/j.cageo.2010.11.010",
  URL =          "http://www.sciencedirect.com/science/article/B6V7D-51NNPFY-3/2/a9730f48b501cdb19b0197c8197ea42c",
  keywords =     "genetic algorithms, genetic programming, Groundwater
                 depth fluctuation, Neuro-fuzzy, Forecast",
  abstract =     "This paper investigates the ability of genetic
                 programming (GP) and adaptive neuro-fuzzy inference
                 system (ANFIS) techniques for groundwater depth
                 forecasting. Five different GP and ANFIS models
                 comprising various combinations of water table depth
                 values from two stations, Bondville and Perry, are
                 developed to forecast one-, two- and three-day ahead
                 water table depths. The root mean square errors (RMSE),
                 scatter index (SI), Variance account for (VAF) and
                 coefficient of determination (R2) statistics are used
                 for evaluating the accuracy of models. Based on the
                 comparisons, it was found that the GP and ANFIS models
                 could be employed successfully in forecasting water
                 table depth fluctuations. However, GP is superior to
                 ANFIS in giving explicit expressions for the problem.",
  notes =        "See \cite{Beriro2012}",
}

Genetic Programming entries for Jalal Shiri Ozgur Kisi

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