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Prediction of Air Quality Indices by Neural Networks and Fuzzy Inference Systems – The Case of Pardubice Microregion

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Engineering Applications of Neural Networks (EANN 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 383))

Abstract

This paper presents a design of models for air quality prediction using feed-forward neural networks of perceptron and Takagi-Sugeno fuzzy inference systems. In addition, the sets of input variables are optimized for each air pollutant prediction by genetic algorithms. Based on data measured by the monitoring station of the Pardubice city, the Czech Republic, models are designed to predict air quality indices for each air pollutant separately and consequently, to predict the common air quality index. Considering the root mean squared error, the results show that the compositions of individual prediction models outperform single predictions of common air quality index. Therefore, these models can be applied to obtain more accurate one day ahead predictions of air quality indices.

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Author information

Authors and Affiliations

  1. Institute of System Engineering and Informatics, Faculty of Economics and Administration, University of Pardubice, Studentská 84, 532 10, Pardubice, Czech Republic

    Petr Hájek & Vladimír Olej

Authors
  1. Petr Hájek
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  2. Vladimír Olej
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Editor information

Editors and Affiliations

  1. Department of Forestry & Management of the Environment and Natural Resources, Democritus University of Thrace, GR-68200, Orestiada, Hellas

    Lazaros Iliadis

  2. Frederick University of Cyprus, Cyprus

    Harris Papadopoulos

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

    Chrisina Jayne

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Hájek, P., Olej, V. (2013). Prediction of Air Quality Indices by Neural Networks and Fuzzy Inference Systems – The Case of Pardubice Microregion. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2013. Communications in Computer and Information Science, vol 383. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41013-0_31

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  • DOI: https://doi.org/10.1007/978-3-642-41013-0_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41012-3

  • Online ISBN: 978-3-642-41013-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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