Assessing Suitability of GP Modeling for Groundwater Level

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@Article{Sivapragasam:2015:APa,
  author =       "C. Sivapragasam and K. Kannabiran and G. Karthik and 
                 S. Raja",
  title =        "Assessing Suitability of {GP} Modeling for Groundwater
                 Level",
  journal =      "Aquatic Procedia",
  volume =       "4",
  pages =        "693--699",
  year =         "2015",
  ISSN =         "2214-241X",
  DOI =          "doi:10.1016/j.aqpro.2015.02.089",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2214241X15000905",
  note =         "International conference on water resources, coastal
                 and ocean engineering, ICWRCOE'15",
  abstract =     "Artificial Neural Network and other soft computing
                 techniques have been widely used for modelling
                 groundwater level changes. Emphasis has been laid by
                 different researches in improving the quality of input
                 data which significantly affects the final model. In
                 this study Genetic Programming (GP) is used to model
                 spatial variation of groundwater in Arjuna Nadhi sub
                 basin region. For a limited list of monthly groundwater
                 level data the result indicates that when information
                 from neighbouring wells, which are selected on their
                 appropriation, is incorporated, the modeling accuracy
                 improves significantly. It is also concluded that each
                 region/ zone needs individual modeling irrespective of
                 their geographic proximity.",
  keywords =     "genetic algorithms, genetic programming, groundwater
                 level, spatial variation, input selection",
}

Genetic Programming entries for C Sivapragasam K Kannabiran G Karthik S Raja

Citations