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Nonlinearity and Prediction of Air Pollution

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Book cover Artificial Neural Nets and Genetic Algorithms

Abstract

A presence of nonlinearity in time series of concentrations of air pollutants and in their relations to time series of meteorological variables is tested using information-theoretic functionals and the surrogate data approach. The results are discussed in relation to predictability of the pollutant concentrations aimed to alert smog episodes.

This work was supported by European Union Fifth Framework Programme project APPETISE (IST-99-11764)

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References

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Author information

Authors and Affiliations

  1. Institute of Computer Science, Academy of Sciences of the Czech Republic, Pod vodárenskou věží 2, 182 07, Prague 8, Czech Republic

    Pavel Juruš

Authors
  1. Milan Paluš
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  2. Emil Pelikán
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  3. Kryštof Eben
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  4. Pavel Krejčíř
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  5. Pavel Juruš
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Editor information

Editors and Affiliations

  1. Institute of Computer Science, Czech Republic

    Věra Kůrková  & Roman Neruda  & 

  2. Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Prague, Czech Republic

    Miroslav Kárný

  3. Division of Mathematics, School of Mathematical and Information Sciences Coventry University, Coventry, UK

    Nigel C. Steele

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© 2001 Springer-Verlag Wien

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Paluš, M., Pelikán, E., Eben, K., Krejčíř, P., Juruš, P. (2001). Nonlinearity and Prediction of Air Pollution. In: Kůrková, V., Neruda, R., Kárný, M., Steele, N.C. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6230-9_118

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  • DOI: https://doi.org/10.1007/978-3-7091-6230-9_118

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83651-4

  • Online ISBN: 978-3-7091-6230-9

  • eBook Packages: Springer Book Archive

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