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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Commun. ACM 37(3), 77–84 (1994)
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)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, Chichester (2002)
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)
Fyfe, C., Baddeley, R.: Non-linear data structure extraction using simple Hebbian networks. Biological Cybernetics 72(6), 533–541 (1995)
Oja, E.: Neural Networks, Principal Components and Subspaces. International Journal of Neural Systems 1, 61–68 (1989)
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)
Harman, H.: Modern Factor Analysis, 2nd edn. University of Chicago Press, Chicago (1967)
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)
Seung, S., Socci, N.D., Lee, D.: The Rectified Gaussian Distribution. Advances in Neural Information. Processing Systems 10, 350–356 (1998)
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)
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: 1029-7006
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arroyo, Á., Corchado, E., Tricio, V. (2010). Soft Computing Models for an Environmental Application. In: Corchado, E., Novais, P., Analide, C., Sedano, J. (eds) Soft Computing Models in Industrial and Environmental Applications, 5th International Workshop (SOCO 2010). Advances in Intelligent and Soft Computing, vol 73. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13161-5_17
Download citation
DOI: https://doi.org/10.1007/978-3-642-13161-5_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13160-8
Online ISBN: 978-3-642-13161-5
eBook Packages: EngineeringEngineering (R0)