Evolving Systems

, Volume 4, Issue 4, pp 221–233 | Cite as

Neurocomputing techniques to dynamically forecast spatiotemporal air pollution data

Original Paper
First Online:
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Abstract

Real time monitoring, forecasting and modeling air pollutants’ concentrations in major urban centers is one of the top priorities of all local and national authorities globally. This paper studies and analyzes the parameters related to the problem, aiming in the design and development of an effective machine learning model and its corresponding system, capable of forecasting dangerous levels of ozone (O3) concentrations in the city center of Athens and more specifically in the “Athinas” air quality monitoring station. This is a multi parametric case, so an effort has been made to combine a vast number of data vectors from several operational nearby measurements’ stations. The final result was the design and construction of a group of artificial neural networks capable of estimating O3 concentrations in real time mode and also having the capacity of forecasting the same values for future time intervals of 1, 2, 3 and 6 h, respectively.

Keywords

Artificial neural networks Machine learning Multi parametric ANN Pollution of the atmosphere Ozone estimation and forecasting 
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References

  1. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, USAMATHGoogle Scholar
  2. Iliadis L (2007) Intelligent information systems and applications in risk estimation. Herodotus Publications, Thessaloniki, GreeceGoogle Scholar
  3. Iliadis L, Spartalis S, Paschalidou A, Kassomenos P (2007) Artificial neural network modeling of the surface ozone concentration. Int J Comput Appl Math 2(2):125–138Google Scholar
  4. Inal F (2010) Artificial neural network prediction of tropospheric ozone concentrations in Istanbul, Turkey. CLEAN Soil Air Water 38(10):897–908MathSciNetCrossRefGoogle Scholar
  5. Jones MT (2005) AI application programming, 2nd edn. Charles River Media, Hingham, MAGoogle Scholar
  6. Kecman V (2001) Learning and soft computing. MIT Press, USAMATHGoogle Scholar
  7. Ministry of Environment, Energy & Climate Change (2010) Air quality, reports, air pollution 2009 annual report. http://www.ypeka.gr/LinkClick.aspx?fileticket=NIpIqbQg
  8. Ministry of Environment, Energy & Climate Change (2011) Air quality, reports, air pollution 2010 annual report, April 2011. http://www.ypeka.gr/LinkClick.aspx?fileticket=5mmiFX%2b4n2M%3d&tabid=490&language=el-GR
  9. Ministry of Environment, Energy & Climate Change (2012) Air pollution measurements. http://www.ypeka.gr/Default.aspx?tabid=495&language=el-GR. Valid at 15 Apr 2012
  10. Ozcan HK, Bilgili E, Sahin U, Bayat C (2007) Modeling of tropospheric ozone concentrations using genetically trained multi-level cellular neural networks. Adv Atmos Sci 24(5):907–914CrossRefGoogle Scholar
  11. Ozdemir H, Demir G, Altay G, Albayrak S, Bayat C (2008) Prediction of tropospheric ozone concentration by employing artificial neural networks. Environ Eng Sci 25(9):1249–1254Google Scholar
  12. Paoli C (2011) A neural network model forecasting for prediction of hourly ozone concentration in Corsica. In: Proceedings IEEE of the 10th international conference on environment and electrical engineering (EEEIC)Google Scholar
  13. Papaleonidas A, Iliadis L (2012a) Employing ANN that estimate ozone in a short-term scale when monitoring stations malfunction. In: Proceedings of the 13th international conference engineering applications of neural networks (EANN 2012), CCIS311, pp 71–80Google Scholar
  14. Papaleonidas A, Iliadis L (2012b) Hybrid and reinforcement multi agent technology for real time air pollution monitoring. In: Proceedings of the 8th IFIP WG12.5 artificial intelligence applications and innovations (AIAI 2012), AICT 381, pp 274–284Google Scholar
  15. Paschalidou A, Iliadis L, Kassomenos P, Bezirtzoglou C (2007) Neural modeling of the tropospheric ozone concentrations in an urban site. In: Proceedings of the 10th international conference engineering applications of neural networks, pp 436–445Google Scholar
  16. Taylor JB (2006) Methods and procedures for the verification and validation of artificial neural networks. Springer Publications, BerlinGoogle Scholar
  17. Wahab ASA, Al-Alawi SM (2002) Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environ Model Softw 17:219–228Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Forestry & Management of the Environment & Natural ResourcesDemocritus University of ThraceOrestiadaGreece

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