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Soft Computing Models for an Environmental Application

  • Ángel Arroyo
  • Emilio Corchado
  • Verónica Tricio
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)

Abstract

In this interdisciplinary research several statistical and soft computing models are applied to analyze a case study related to inmissions of atmospheric pollution in urban areas. The research analyzes the impact on atmospheric pollution of an extended bank holiday weekend in Spain and the way in which meteorological conditions affect pollution levels. After classifying atmospheric pollution levels in relation to the days of the week, we analyze the way in which these may be influenced by atmospheric conditions. The case study is based on data collected by a station at the city of Burgos, which forms part of the pollution measurement station network within the Spanish Autonomous Region of Castile-Leon.

Keywords

Artificial neural networks soft computing meteorology atmospheric pollution statistical models 

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References

  1. 1.
    Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Commun. ACM 37(3), 77–84 (1994)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Subudhi, B., Morris, A.S.: Soft computing methods applied to the control of a flexible robot manipulator. Applied Soft Computing 9(1), 149–158 (2009)CrossRefGoogle Scholar
  3. 3.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, Chichester (2002)Google Scholar
  4. 4.
    Oja, E., Ogawa, H., Wangviwattana, J.: Principal Components Analysis by Homogeneous Neural Networks, part 1. The Weighted Subspace Criterion. IEICE Transaction on Information and Systems E75D, 366–375 (1992)Google Scholar
  5. 5.
    Fyfe, C., Baddeley, R.: Non-linear data structure extraction using simple Hebbian networks. Biological Cybernetics 72(6), 533–541 (1995)zbMATHCrossRefGoogle Scholar
  6. 6.
    Oja, E.: Neural Networks, Principal Components and Subspaces. International Journal of Neural Systems 1, 61–68 (1989)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. In: Proc. of the 10th European Symposium on Artificial Neural Networks (ESANN 2002), pp. 143–148 (2002)Google Scholar
  8. 8.
    Harman, H.: Modern Factor Analysis, 2nd edn. University of Chicago Press, Chicago (1967)zbMATHGoogle Scholar
  9. 9.
    Corchado, E., MacDonald, D., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit. Data Min. Knowl. Discov. 8(3), 203–225 (2004)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Seung, S., Socci, N.D., Lee, D.: The Rectified Gaussian Distribution. Advances in Neural Information. Processing Systems 10, 350–356 (1998)Google Scholar
  11. 11.
    Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. In: Proc. of the 10th European Symposium on Artificial Neural Networks (ESANN 2002), pp. 143–148 (2002)Google Scholar
  12. 12.
    Tricio, V., Viloria, R., Minguito, A: Ozone Measurements. In: Urban and Semi-Rural Sites At Burgos (Spain). Geophysical Research Abstracts, vol. 5 (2003); EGS-AGU-EUG Joint Assembly EAE03-A-14249. ISSN: 1029-7006Google Scholar
  13. 13.
    Arroyo, A., Corchado, E., Tricio, V.: Atmospheric Pollution Analysis by Unsupervised Learning. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 767–772. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Arroyo, A., Corchado, E., Tricio, V.: Computational Methods for Immision Analysis of Urban Atmospheric Pollution. In: 9th International Conference Computational and Mathematical Methods in science and engineering, Gijón (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ángel Arroyo
    • 1
  • Emilio Corchado
    • 2
  • Verónica Tricio
    • 3
  1. 1.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  2. 2.Department of Computer Science and AutomaticUniversity of SalamancaSalamancaSpain
  3. 3.Department of PhysicsUniversity of BurgosBurgosSpain

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