Prediction and simulation of monthly groundwater levels by genetic programming

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@Article{FallahMehdipour:2013:JHR,
  author =       "E. Fallah-Mehdipour and O. {Bozorg Haddad} and 
                 M. A. Marino",
  title =        "Prediction and simulation of monthly groundwater
                 levels by genetic programming",
  journal =      "Journal of Hydro-environment Research",
  year =         "2013",
  volume =       "7",
  number =       "4",
  pages =        "253--260",
  keywords =     "genetic algorithms, genetic programming, Adaptive
                 neural fuzzy inference system, Prediction, Simulation,
                 Groundwater level",
  ISSN =         "1570-6443",
  DOI =          "doi:10.1016/j.jher.2013.03.005",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1570644313000270",
  abstract =     "Groundwater level is an effective parameter in the
                 determination of accuracy in groundwater modelling.
                 Thus, application of simple tools to predict future
                 groundwater levels and fill-in gaps in data sets are
                 important issues in groundwater hydrology. Prediction
                 and simulation are two approaches that use previous and
                 previous-current data sets to complete time series.
                 Artificial intelligence is a computing method that is
                 capable to predict and simulate different system states
                 without using complex relations. This paper
                 investigates the capability of an adaptive neural fuzzy
                 inference system (ANFIS) and genetic programming (GP)
                 as two artificial intelligence tools to predict and
                 simulate groundwater levels in three observation wells
                 in the Karaj plain of Iran. Precipitation and
                 evaporation from a surface water body and water levels
                 in observation wells penetrating an aquifer system are
                 used to fill-in gaps in data sets and estimate monthly
                 groundwater level series. Results show that GP
                 decreases the average value of root mean squared error
                 (RMSE) as the error criterion for the observation wells
                 in the training and testing data sets 8.35 and 11.33
                 percent, respectively, compared to the average of RMSE
                 by ANFIS in prediction. Similarly, the average value of
                 RMSE for different observation wells used in simulation
                 improves the accuracy of prediction 9.89 and 8.40
                 percent in the training and testing data sets,
                 respectively. These results indicate that the proposed
                 prediction and simulation approach, based on GP, is an
                 effective tool in determining groundwater levels.",
}

Genetic Programming entries for Elahe Fallah-Mehdipour Omid Bozorg Haddad Miguel A Marino

Citations