Climatic variation of the structure of maximum daily temperatures in Spain: A combined statistical and computational intelligence approach

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@InProceedings{Valdes:2009:IJCNN,
  author =       "Julio J. Valdes and Antonio Pou",
  title =        "Climatic variation of the structure of maximum daily
                 temperatures in Spain: A combined statistical and
                 computational intelligence approach",
  booktitle =    "International Joint Conference on Neural Networks,
                 IJCNN 2009",
  year =         "2009",
  month =        "14-19 " # jun,
  address =      "Atlanta, Georgia, US",
  pages =        "3172--3179",
  keywords =     "genetic algorithms, genetic programming, Iberian
                 Peninsula, Mediterranean climates, Spanish
                 meteorological stations, classical optimization,
                 climatic variation, computational intelligence
                 approach, geographical distribution, maximum daily
                 temperatures, multimodal empirical distribution
                 function, statistical approach, artificial
                 intelligence, climatology, geophysics computing,
                 meteorology, statistical distributions",
  DOI =          "doi:10.1109/IJCNN.2009.5178649",
  ISSN =         "1098-7576",
  abstract =     "Two blocks (1904-1921 and 1990-2007) of daily maximum
                 temperature data from seventeen Spanish meteorological
                 stations exhibit a multimodal empirical distribution
                 function (EDF). Most of the stations show important
                 differences in their EDF for each one of the considered
                 periods of time, a fact that reveals the complexity of
                 climatic changes within the accepted general warming
                 trend of the Iberian Peninsula.

                 As a tentative approach to understand the underlying
                 structure of data, each EDF has been decomposed on two
                 normal distributed functions. The parameters describing
                 these functions for each station and for each time
                 period have been space-optimized and visualized using
                 classical optimization and genetic programming. The
                 changes in the geographical distribution of the classes
                 derived from the analysis point towards a recent
                 greater role of Mediterranean climates, spreading its
                 influence to the interior of the Peninsula. The general
                 picture, however, is much more complex than a linear
                 warming and a number of stations even show negative
                 trends.

                 This study is considered to be a preliminary
                 methodological exploration of future procedures
                 destined to close the gap between data driven analysis
                 and what models based upon first principles may tell.",
  notes =        "Also known as \cite{5178649}",
}

Genetic Programming entries for Julio J Valdes Antonio Pou

Citations