Genetic Programming-Based Model Output Statistics for Short-Range Temperature Prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{Seo:evoapps13,
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author = "Kisung Seo and Byeongyong Hyeon and Soohwan Hyun and
Younghee Lee",
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title = "Genetic Programming-Based Model Output Statistics for
Short-Range Temperature Prediction",
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booktitle = "Applications of Evolutionary Computing,
EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
EvoRISK, EvoROBOT, EvoSTOC",
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year = "2013",
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month = "3-5 " # apr,
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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",
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series = "LNCS",
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volume = "7835",
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publisher = "Springer Verlag",
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address = "Vienna",
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publisher_address = "Berlin",
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pages = "122--131",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming, temperature
forecast, MOS, UM, KLAPS",
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isbn13 = "978-3-642-37191-2",
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DOI = "doi:10.1007/978-3-642-37192-9_13",
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size = "10 pages",
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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.",
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notes = "
EvoApplications2013 held in conjunction with
EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",
- }
Genetic Programming entries for
Kisung Seo
Byeongyong Hyeon
Soohwan Hyun
Younghee Lee
Citations