Predicting groundwater level fluctuations with meteorological effect implications-A comparative study among soft computing techniques

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@Article{Shiri:2013:CG,
  author =       "Jalal Shiri and Ozgur Kisi and Heesung Yoon and 
                 Kang-Kun Lee and Amir Hossein Nazemi",
  title =        "Predicting groundwater level fluctuations with
                 meteorological effect implications-A comparative study
                 among soft computing techniques",
  journal =      "Computer \& Geosciences",
  volume =       "56",
  pages =        "32--44",
  year =         "2013",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, Groundwater level fluctuations,
                 Prediction, Artificial intelligence techniques, ARMA",
  ISSN =         "0098-3004",
  DOI =          "doi:10.1016/j.cageo.2013.01.007",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0098300413000216",
  abstract =     "The knowledge of groundwater table fluctuations is
                 important in agricultural lands as well as in the
                 studies related to groundwater use and management
                 levels. This paper investigates the abilities of Gene
                 Expression Programming (GEP), Adaptive Neuro-Fuzzy
                 Inference System (ANFIS), Artificial Neural Networks
                 (ANN) and Support Vector Machine (SVM) techniques for
                 groundwater level forecasting in following day up to
                 7-day prediction intervals. Several input combinations
                 comprising water table level, rainfall and
                 evapotranspiration values from Hongcheon Well station
                 (South Korea), covering a period of eight years
                 (2001-2008) were used to develop and test the applied
                 models. The data from the first six years were used for
                 developing (training) the applied models and the last
                 two years data were reserved for testing. A comparison
                 was also made between the forecasts provided by these
                 models and the Auto-Regressive Moving Average (ARMA)
                 technique. Based on the comparisons, it was found that
                 the GEP models could be employed successfully in
                 forecasting water table level fluctuations up to 7 days
                 beyond data records.",
}

Genetic Programming entries for Jalal Shiri Ozgur Kisi Heesung Yoon Kang-Kun Lee Amir Hossein Nazemi

Citations