Semi-supervised Hybrid Modeling of Atmospheric Pollution in Urban Centers

  • Ilias Bougoudis
  • Konstantinos Demertzis
  • Lazaros Iliadis
  • Vardis-Dimitris Anezakis
  • Antonios Papaleonidas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 629)

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.

Keywords

Pollution of the atmosphere Air quality Semi-supervised learning Semi-supervised clustering Semi-supervised classification Air pollution 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ilias Bougoudis
    • 1
  • Konstantinos Demertzis
    • 1
  • Lazaros Iliadis
    • 1
  • Vardis-Dimitris Anezakis
    • 1
  • Antonios Papaleonidas
    • 1
  1. 1.Democritus University of ThraceOrestiadaGreece

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