Characterization of Climatic Variations in Spain at the Regional Scale: A Computational Intelligence Approach

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

@InProceedings{Valdes:2008:ijcnn,
  author =       "Julio J. Valdes and Antonio Pou and Robert Orchard",
  title =        "Characterization of Climatic Variations in Spain at
                 the Regional Scale: A Computational Intelligence
                 Approach",
  booktitle =    "2008 IEEE World Congress on Computational
                 Intelligence",
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "470--476",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1821-3",
  file =         "NN0185.pdf",
  DOI =          "doi:10.1109/IJCNN.2008.4633834",
  ISSN =         "1098-7576",
  keywords =     "genetic algorithms, genetic programming,
                 Fletcher-Reeves optimization, Kolmogorov-Smirnov
                 dissimilarity analysis, Spain, cluster analysis,
                 computational intelligence approach, data mining,
                 differential evolution, hybrid optimization, principal
                 component analysis, regional climatic variation,
                 similarity-preservation feature generation,
                 time-varying climatic variation characterization,
                 climatology, data mining, geophysical techniques,
                 geophysics computing, pattern clustering, principal
                 component analysis",
  abstract =     "Computational intelligence and other data mining
                 techniques are used for characterising regional and
                 time varying climatic variations in Spain in the period
                 1901-2005. Daily maximum temperature data from 10
                 climatic stations are analysed (with and without
                 missing values) using principal components (PC),
                 similarity-preservation feature generation, clustering,
                 Kolmogorov-Smirnov dissimilarity analysis and genetic
                 programming (GP). The new features were computed using
                 hybrid optimisation (differential evolution and
                 Fletcher- Reeves) and GP. From them, a scalar regional
                 climatic index was obtained which identifies time
                 landmarks and changes in the climate rhythm. The
                 equations obtained with GP are simpler than those
                 obtained with PC and they highlight the most important
                 sites characterising the regional climate. Whereas the
                 general consensus is that there has been a clear and
                 smooth trend towards warming during the last decades,
                 the results suggest that the picture may probably be
                 much more complicated than what is usually assumed.",
  notes =        "Also known as \cite{4633834} WCCI 2008 - A joint
                 meeting of the IEEE, the INNS, the EPS and the IET.",
}

Genetic Programming entries for Julio J Valdes Antonio Pou Robert Orchard

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