The Prediction of Tropospheric Ozone Using a Radial Basis Function Network

  • Kříž RadkoEmail author
  • Šedek Pavel
Part of the Emergence, Complexity and Computation book series (ECC, volume 14)


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.


Tropospheric ozone Time series analysis Artificial neural network Prediction Radial basis function network 


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  1. 1.
    Abdul-Wahab, S.A., Al-Alawi, S.M.: Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environmental Modelling & Software 17(3), 219–228 (2002)CrossRefGoogle Scholar
  2. 2.
    Tropospheric ozone in the European Union, The consolidated Report, Luxembourg (1999)Google Scholar
  3. 3.
    Baťa, R., Půlkrábková, P.: The importance of Modelling the Environmental Impacts of a Biomass Based Electric Power Generation for public safety. WSEAS Transactions on Environment & Development 9(4) (2013)Google Scholar
  4. 4.
    Kříž, R.: Chaos in Nitrogen Dioxide Concentration Time Series and Its Prediction. In: Nostradamus 2014: Prediction, Modeling and Analysis of Complex Systems, pp. 365–376. Springer International Publishing (2014)Google Scholar
  5. 5.
    Health Aspects of Air Pollution with Particulate Matter, Ozone and Nitrogen Dioxide. WHO-Europe report, Bonn (2003)Google Scholar
  6. 6.
    Cobourn, W.G., Dolcine, L., French, M., Hubbard, M.C.: A comparison of nonlinear regression and neural network models for ground-level ozone forecasting. Journal of the Air & Waste Management Association 50(11), 1999–2009 (2000)CrossRefGoogle Scholar
  7. 7.
    Sousa, S.I.V., Martins, F.G., Alvim-Ferraz, M.C.M., Pereira, M.C.: Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environmental Modelling & Software 22(1), 97–103 (2007)CrossRefGoogle Scholar
  8. 8.
    Bandyopadhyay, G., Chattopadhyay, S.: Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone. International Journal of Environmental Science & Technology 4, 141–149 (2007)CrossRefGoogle Scholar
  9. 9.
    Ozdemir, H., et al.: Prediction of tropospheric ozone concentration by employing artificial neural networks. Environmental Engineering Science 25(9), 1249–1254 (2008)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Al-Alawi, S.M., Abdul-Wahab, S.A., Bakheit, C.S.: Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone. Environmental Modelling & Software 23(4), 396–403 (2008)CrossRefGoogle Scholar
  11. 11.
    Yi, J., Prybutok, V.R.: A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. Environmental Pollution 92(3), 349–357 (1996)CrossRefGoogle Scholar
  12. 12.
    Yi, J., Prybutok, R.: A Neural Network Model Forecasting for Prediction of Daily Maximum Ozone Concentration in an Industrialized Urban Area. Environ. Pollut. 92, 349–357 (1996)CrossRefGoogle Scholar
  13. 13.
    Gurney, K.: An introduction to neural networks. CRC Press (1997)Google Scholar
  14. 14.
    Artificial Neural Networks. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning SE - 35, p. 44. Springer, US (2010),, doi:10.1007/978-0-387-30164-8_35, ISBN: 978-0-387-30768-8
  15. 15.
  16. 16.
    Broomhead, D.S., Lowe, D.: Radial basis functions, multi-variable functional interpolation and adaptive networks. No. RSRE-MEMO-4148. Royal Signals And Radar Establishment Malvern, United Kingdom (1988)Google Scholar
  17. 17.
    Sundararajan, N., Saratchandran, P., Lu, Y.W.: Radial Basis Function Neural Networks with Sequential Learning: MRAN and Its Applications. Progress in neural processing. World Scientific (1999)Google Scholar
  18. 18.
  19. 19.
    Mathworks Inc.: MATLAB Functions in Neural Network ToolboxGoogle Scholar
  20. 20.
    Mathworks Inc.: Radial Basis Neural Networks – MATLAB & SimulinkGoogle Scholar

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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Economics and Administration, Institute of Administrative and Social SciencesUniversity of PardubicePardubiceCzech Republic
  2. 2.Faculty of Electrical Engineering, Dept. of Economics, Management and HumanitiesCzech Technical UniversityPrahaCzech Republic

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