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2016 | OriginalPaper | Chapter

Semi-supervised Hybrid Modeling of Atmospheric Pollution in Urban Centers

Authors : Ilias Bougoudis, Konstantinos Demertzis, Lazaros Iliadis, Vardis-Dimitris Anezakis, Antonios Papaleonidas

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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Abstract

Air pollution is directly linked with the development of technology and science, the progress of which besides significant benefits to mankind it also has adverse effects on the environment and hence on human health. The problem has begun to take worrying proportions especially in large urban centers, where 60,000 deaths are reported each year in Europe’s towns and 3,000,000 worldwide, due to long-term air pollution exposure (exposure of the European Agency for the Environment http://​www.​eea.​europa.​eu/​). In this paper we propose a novel and flexible hybrid machine learning system that combines Semi-Supervised Classification and Semi-Supervised Clustering, in order to realize prediction of air pollutants outliers and to study the conditions that favor their high concentration.

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previous chapter Choice of Best Samples for Building Ensembles in Dynamic Environments
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Metadata
Title
Semi-supervised Hybrid Modeling of Atmospheric Pollution in Urban Centers
Authors
Ilias Bougoudis
Konstantinos Demertzis
Lazaros Iliadis
Vardis-Dimitris Anezakis
Antonios Papaleonidas
Copyright Year
2016
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-319-44187-0

Electronic ISBN: 978-3-319-44188-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-44188-7_4

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