Abstract
This paper presents a multidisciplinary study on the application of statistical and neural models for analysing data on immissions of atmospheric pollution in urban areas. Data was collected from the network of pollution measurement stations in the Spanish Autonomous Region of Castile-Leon. Four pollution parameters and a pollution measurement station in the city of Burgos were used to carry out the study in 2007, during a period of just over six months. Pollution data are compared, their values are interrelated and relationships are established not only with the pollution variables, but also with different weeks of the year. The aim of this study is to classify the levels of atmospheric pollution in relation to the days of the week, trying to differentiate between working days and non-working days.
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
van der Maaten, L.J.P.: An Introduction to Dimensionality Reduction Using Matlab. Report MICC 07-07
Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24, 417–441 (1933)
Pearson, K.: On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine 2, 559–572 (1901)
Everitt, B.: An R and S-PLUS companion to multivariate analysis. Springer, Heidelberg (2005)
Tenenbaum, B., de Silva, V., Langford, J.C.: A Globlal Geometric framework for Nonlinear Dimensionality Reduction. Science 290(5500), 2319–2323 (2000)
Floyd, R.W.: Algorithm 97: Shortest path. Communications of the ACMÂ 5(6), 345 (1962)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Chan, H.–P., Yeung, D.–Y., Xiong, Y.: Super – resolution throuugh neighbor embedding. In: IEEE Computer Society Conference on computer vision and pattern recognition, vol. 1, pp. 275–282 (2004)
Corchado, E., MacDonald, D., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit. Data Mining and Knowledge Discovery 8(3) (2004)
Tricio, V., Viloria, R., Minguito, A.: Evolución del ozono en Burgos y provincia a partir de los datos de la red de medida de contaminación atmosférica. Los retos del desar-rollo sostenible en España. In: Informe CONAMA 2006, 31 pages (2006), http://www.conama8.org/modulodocumentos/documentos/CTs/CT86.pdf
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 (in Press, 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Arroyo, A., Corchado, E., Tricio, V. (2009). Atmospheric Pollution Analysis by Unsupervised Learning. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_94
Download citation
DOI: https://doi.org/10.1007/978-3-642-04394-9_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04393-2
Online ISBN: 978-3-642-04394-9
eBook Packages: Computer ScienceComputer Science (R0)