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Fuzzy Inference ANN Ensembles for Air Pollutants Modeling in a Major Urban Area: The Case of Athens

  • Conference paper
Engineering Applications of Neural Networks (EANN 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 459))

Abstract

All over the globe, major urban centers face a significant air pollution problem, which is becoming worse every year. This research effort aims to contribute towards real time monitoring of air quality, which is a target of great importance for people’s health. However, a serious obstacle is the high percentage of erroneous or missing data which is highly prolonged in many of the cases. To overcome this problem and due to the individuality of each residential area of Athens, separate local ANN had to be developed, capable of performing reliable interpolation of missing data vectors on an hourly basis. Also due to the need for hourly overall estimations of pollutants in the wider area of a major city, ANN ensembles were additionally developed by employing four existing methods and an innovative fuzzy inference approach.

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Author information

Authors and Affiliations

  1. Department of Forestry & Management of the Environment & Natural Resources, Democritus University of Thrace, 193 Pandazidou st., 68200, N Orestiada, Greece

    Ilias Bougoudis, Lazaros Iliadis & Antonis Papaleonidas

Authors
  1. Ilias Bougoudis
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  2. Lazaros Iliadis
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  3. Antonis Papaleonidas
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Editor information

Editors and Affiliations

  1. Dept. of Theoretical Electrical Engineering, Technical University of Sofia, 8, Kliment Ohridski St., 1000, Sofia, Bulgaria

    Valeri Mladenov

  2. Faculty of Engineering and Computing, Coventry University, UK

    Chrisina Jayne

  3. Department of Forestry and Management of the Environment, Democritus University of Thrace, Pandazidou 193, 68200, Orestiada, Greece

    Lazaros Iliadis

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Bougoudis, I., Iliadis, L., Papaleonidas, A. (2014). Fuzzy Inference ANN Ensembles for Air Pollutants Modeling in a Major Urban Area: The Case of Athens. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-11071-4_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11070-7

  • Online ISBN: 978-3-319-11071-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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