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Support Vector Regression Algorithms in the Forecasting of Daily Maximums of Tropospheric Ozone Concentration in Madrid

  • E. G. Ortiz-García
  • S. Salcedo-Sanz
  • A. M. Pérez-Bellido
  • J. Gascón-Moreno
  • A. Portilla-Figueras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

In this paper we present the application of a support vector regression algorithm to a real problem of maximum daily tropospheric ozone forecast. The support vector regression approach proposed is hybridized with an heuristic for optimal selection of hyper-parameters. The prediction of maximum daily ozone is carried out in all the station of the air quality monitoring network of Madrid. In the paper we analyze how the ozone prediction depends on meteorological variables such as solar radiation and temperature, and also we perform a comparison against the results obtained using a multi-layer perceptron neural network in the same prediction problem.

Keywords

Support Vector Machine Support Vector Regression Meteorological Variable Multilayer Perceptron Tropospheric Ozone 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • E. G. Ortiz-García
    • 1
  • S. Salcedo-Sanz
    • 1
  • A. M. Pérez-Bellido
    • 1
  • J. Gascón-Moreno
    • 1
  • A. Portilla-Figueras
    • 1
  1. 1.Universidad de AlcaláMadridSpain

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