Genetic Programming-Based Model Output Statistics for Short-Range Temperature Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Seo:evoapps13,
  author =       "Kisung Seo and Byeongyong Hyeon and Soohwan Hyun and 
                 Younghee Lee",
  title =        "Genetic Programming-Based Model Output Statistics for
                 Short-Range Temperature Prediction",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
                 EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
                 EvoRISK, EvoROBOT, EvoSTOC",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and 
                 Ivanoe {De Falco} and Ernesto Tarantino and 
                 Carlos Cotta and Robert Schaefer and Konrad Diwold and 
                 Kyrre Glette and Andrea Tettamanzi and 
                 Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and 
                 Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and 
                 Aniko Ekart and Francisco {Fernandez de Vega} and 
                 Sara Silva and Evert Haasdijk and Gusz Eiben and 
                 Anabela Simoes and Philipp Rohlfshagen",
  series =       "LNCS",
  volume =       "7835",
  publisher =    "Springer Verlag",
  address =      "Vienna",
  publisher_address = "Berlin",
  pages =        "122--131",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, temperature
                 forecast, MOS, UM, KLAPS",
  isbn13 =       "978-3-642-37191-2",
  DOI =          "doi:10.1007/978-3-642-37192-9_13",
  size =         "10 pages",
  abstract =     "his paper introduces GP (Genetic Programming) based
                 robust compensation technique for temperature
                 prediction in short-range. MOS (Model Output
                 Statistics) is a statistical technique that corrects
                 the systematic errors of the model. Development of an
                 efficient MOS is very important, but most of MOS are
                 based on the idea of relating model forecasts to
                 observations through a linear regression. Therefore it
                 is hard to manage complex and irregular natures of the
                 prediction. In order to solve the problem, a nonlinear
                 and symbolic regression method using GP is suggested as
                 the first attempt. The purpose of this study is to
                 evaluate the accuracy of the estimation by GP based
                 nonlinear MOS for the 3 days temperatures for Korean
                 regions. This method is then compared to the UM model
                 and shows superior results. The training period of
                 summer in 2007-2009 is used, and the data of 2010
                 summer is adopted for verification.",
  notes =        "http://www.kevinsim.co.uk/evostar2013/cfpEvoApplications.html
                 EvoApplications2013 held in conjunction with
                 EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",
}

Genetic Programming entries for Kisung Seo Byeongyong Hyeon Soohwan Hyun Younghee Lee

Citations