Fuzzy Inference ANN Ensembles for Air Pollutants Modeling in a Major Urban Area: The Case of Athens

  • Ilias Bougoudis
  • Lazaros Iliadis
  • Antonis Papaleonidas
Part of the Communications in Computer and Information Science book series (CCIS, volume 459)


All over the globe, major urban centers face a significant air pollution problem, which is becoming worse every year. This research effort aims to contribute towards real time monitoring of air quality, which is a target of great importance for people’s health. However, a serious obstacle is the high percentage of erroneous or missing data which is highly prolonged in many of the cases. To overcome this problem and due to the individuality of each residential area of Athens, separate local ANN had to be developed, capable of performing reliable interpolation of missing data vectors on an hourly basis. Also due to the need for hourly overall estimations of pollutants in the wider area of a major city, ANN ensembles were additionally developed by employing four existing methods and an innovative fuzzy inference approach.


ANN ensembles Fuzzy Inference System Air Pollution 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baidyk, T., Kussul, E.: Ensemble Neural Networks. Optical Memory and Neural Networks 18(4), 295–303 (2009)CrossRefGoogle Scholar
  2. 2.
    Chaloulakou, A., Kassomenos, P., Spyrellis, N., Demokritou, P., Koutrakis, P.: Measurements of PM10 and PM2.5 particle concentrations in Athens. Greece Atmospheric Environment 37(2003), 649–660 (2012)Google Scholar
  3. 3.
    Hooyberghs, J., Mensink, C., Dumont, G., Fierens, F., Brasseur, O.: A neural network forecast for daily average PM10 concentrations in Belgium. Atmospheric Environment (January 2005)Google Scholar
  4. 4.
    Iliadis, L.: Intelligent Information Systems and Applications in Risk Estimation. Hrodotos Publications (2007)Google Scholar
  5. 5.
    Inal, F.: Artificial Neural Network Prediction of Tropospheric Ozone Concentrations in Istanbul, Turkey. CLEAN – Soil, Air, Water 38(10), 897–908 (2010)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Jimenez, D.: Dynamically weighted ensemble neural networks for classification (1998)Google Scholar
  7. 7.
    The 1998 IEEE International Joint Conference (Volume: 1) Google Scholar
  8. 8.
    Kadri, C., Tian, F., Zhang, L., Dang, L., Li, G.: Neural Network Ensembles for Online Gas Concentration Estimation Using an Electronic Nose. International Journal of Computer Science Issues 10(2(1)) (March 2013)Google Scholar
  9. 9.
    Lopez, M., Melin, P., Castillo, O.: A method for creating Ensemble Neural Networks using a Sampling Data Approach. In: Thero. Advances and Applications of Fuzzy Logic. ASC, vol. 42, pp. 772–780. Springer (2007)Google Scholar
  10. 10.
    Maclin, R., Opitz, D.: Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research 11, 169–198 (1999)zbMATHGoogle Scholar
  11. 11.
    Mammone, R.J.: Artificial Neural Networks for Speech and Vision, pp. 126–142. Chapman & Hall, London (1993)Google Scholar
  12. 12.
    Marougianni, G.: Forecasting tropospheric ozone levels from meteorological variables: Athens urban area as a case study. Postgraduate thesis. AUTH, Greece (2010)Google Scholar
  13. 13.
    Ministry of Environment, Energy & Climate Change, Air Quality, Reports, Air Pollution 2009 Annual Report (2010)Google Scholar
  14. 14.
    Ozcan, H.K., Bilgili, E., Sahin, U., Bayat, C.: Modeling of trophospheric ozone concentrations using genetically trained multi-level cellular neural networks. Advances in Atmospheric Sciences 24(5), 907–914 (2007)CrossRefGoogle Scholar
  15. 15.
    Ozdemir, H., Demir, G., Altay, G., Albayrak, S., Bayat, C.: Environmental Engineering Science 25(9), 1249–1254 (2008)Google Scholar
  16. 16.
    Ordieres Meré, J.B., Vergara González, E.P., Capuz, R.S., Salaza, R.E.: Neural network prediction model for fine particulate matter (PM). Environmental Modelling and Software 20, 547–559 (2005)CrossRefGoogle Scholar
  17. 17.
    Paoli, C.: 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 (2011)Google Scholar
  18. 18.
    Papaleonidas, A., Iliadis, L.: Employing ANN That Estimate Ozone in a Short-Term Scale When Monitoring Stations Malfunction. In: Jayne, C., Yue, S., Iliadis, L. (eds.) EANN 2012. CCIS, vol. 311, pp. 71–80. Springer, Heidelberg (2012a)CrossRefGoogle Scholar
  19. 19.
    Papaleonidas, A., Iliadis, L.: Hybrid and Reinforcement Multi Agent Technology for real time air pollution monitoring. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) Artificial Intelligence Applications and Innovations. IFIP AICT, vol. 381, pp. 274–284. Springer, Heidelberg (2012b)CrossRefGoogle Scholar
  20. 20.
    Papaleonidas, A., Iliadis, L.: Neurocomputing techniques to dynamically forecast spatiotemporal air pollution data. Evolving Systems 4, 221–233 (2013), doi:10.1007/s12530-013-9078-5CrossRefGoogle Scholar
  21. 21.
    Paschalidou, A., Iliadis, L., Kassomenos, P., Bezirtzoglou, C.: Neural Modeling of the Tropospheric Ozone concentrations in an Urban Site. In: Proceedings of the 10th International Conference Engineering Applications of Neural Networks, pp. 436–445 (2007)Google Scholar
  22. 22.
    Roy, S.: Prediction of Particulate Matter Concentrations Using Artificial Neural Network. Resources and Environment 2(2), 30–36 (2012), doi:10.5923/ Scholar
  23. 23.
    Díaz-Robles, L.A., Ortega, J.C., Fu, J.S., Reed, G.D., Chow, J.C., Watson, J.G., Moncada-Herrera, J.A.: A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile 42(35), 8331–8340 (2008)Google Scholar
  24. 24.
    Sfetsos, A., Vlachogiannis, D.: A new approach to discovering the causal relationship between meteorological patterns and PM10 exceedances. Atmospheric Research 98(2), 500–511 (2013)Google Scholar
  25. 25.
    Slini, T., Karatzas, K., Moussiopoulos, N.: Correlation of air Pollution and Meteorological data Networks. In: 8th Int. Conf. on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes (2002)Google Scholar
  26. 26.
    Wahab, A.-S.A., Al-Alawi, S.M.: Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environmental Modeling & Software 17, 219–228 (2002)CrossRefGoogle Scholar
  27. 27.
    Wolpert, D.: Stacked Generalization. Neural Networks 5, 241–259 (1992)CrossRefGoogle Scholar
  28. 28.
    Zhou, Z.H., Wu, J., Wei, T.: Corrigendum to “Ensembling neural networks: Many could be better than all”. Artificial Intelligence 174(18), 15–70 (2010)CrossRefGoogle Scholar
  29. 29.
    Gardner, M.W., Dorling, S.R.: Artificial Neural Networks (The Multilayer Perceptron) - a Review of Applications in the Atmospheric Sciences. Atmospheric Environment 32(14/15), 2627–2636 (1998)CrossRefGoogle Scholar
  30. 30.
    Kolehmainen, M., Martikainen, H., Ruuskanen, J.: Neural networks and periodic components used in air quality forecasting. Atmospheric Environment 35(5), 815–825 (2001)CrossRefGoogle Scholar
  31. 31.
  32. 32.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ilias Bougoudis
    • 1
  • Lazaros Iliadis
    • 1
  • Antonis Papaleonidas
    • 1
  1. 1.Department of Forestry & Management of the Environment & Natural ResourcesDemocritus University of ThraceN OrestiadaGreece

Personalised recommendations