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.
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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
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DOI: https://doi.org/10.1007/978-3-642-04394-9_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04393-2
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