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The Prediction of Tropospheric Ozone Using a Radial Basis Function Network

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ISCS 2014: Interdisciplinary Symposium on Complex Systems

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 14))

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Abstract

The goal of this paper is to analyze the tropospheric ozone (O3) concentration time series and its prediction using artificial neural networks (ANNs). Tropospheric ozone has harmful effects on human health and on the environment. This study was based on daily averaged tropospheric ozone (O3) data from Pardubice in the Czech Republic. In this study, daily averaged ozone concentrations in Pardubice were predicted using a radial basis function network (RBFN) with three pollutant parameters and three meteorological factors in selected areas. We used a three-layer ANN, which consists of input, hidden, and output layers.

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Author information

Authors and Affiliations

  1. Faculty of Economics and Administration, Institute of Administrative and Social Sciences, University of Pardubice, Studentská 84, 532 10, Pardubice, Czech Republic

    Kříž Radko

  2. Faculty of Electrical Engineering, Dept. of Economics, Management and Humanities, Czech Technical University, Technická 2, 166 27, Praha, Czech Republic

    Šedek Pavel

Authors
  1. Kříž Radko
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  2. Šedek Pavel
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Corresponding author

Correspondence to Kříž Radko .

Editor information

Editors and Affiliations

  1. Institute for Theoretical Physics, Tübingen, Germany

    Ali Sanayei

  2. Institute for Physical and Theoretical Chemistry, University of Tübingen, Tübingen, Germany

    Otto E. Rössler

  3. Department of Computer Science Faculty of Elect.Eng. and Comp. Sci., VŠB-TUO, Ostrava-Poruba, Czech Republic

    Ivan Zelinka

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Radko, K., Pavel, Š. (2015). The Prediction of Tropospheric Ozone Using a Radial Basis Function Network. In: Sanayei, A., E. Rössler, O., Zelinka, I. (eds) ISCS 2014: Interdisciplinary Symposium on Complex Systems. Emergence, Complexity and Computation, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-319-10759-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-10759-2_13

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10758-5

  • Online ISBN: 978-3-319-10759-2

  • eBook Packages: EngineeringEngineering (R0)

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