Abstract
Air pollution is related to the concentration of harmful substances in the lower layers of the atmosphere and it is one of the most serious problems threatening the modern way of life. Determination of the conditions that cause maximization of the problem and assessment of the catalytic effect of relative humidity and temperature are important research subjects in the evaluation of environmental risk. This research effort describes an innovative model towards the forecasting of both primary and secondary air pollutants in the center of Athens, by employing Soft Computing Techniques. More specifically, Fuzzy Cognitive Maps are used to analyze the conditions and to correlate the factors contributing to air pollution. According to the climate change scenarios till 2100, there is going to be a serious fluctuation of the average temperature and rainfall in a global scale. This modeling effort aims in forecasting the evolution of the air pollutants concentrations in Athens as a consequence of the upcoming climate change.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Amer, M., Jetter, A.J., Daim, T.U.: Scenario planning for the national wind energy sector through fuzzy cognitive maps. In: Technology Management in the IT-Driven Services (PICMET) Proceedings of PICMET 2013, pp. 2153–2162 (2013)
Bougoudis, Ι., Demertzis, Κ., Iliadis, L.: HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens. Neural Comput. Appl. 27, 1191–1206 (2015). doi:10.1007/s00521-015-1927-7. Springer
Bougoudis, Ι., Demertzis, Κ., Iliadis, L.: Fast and low cost prediction of extreme air pollution values with hybrid unsupervised learning. In: Integrated Computer-Aided Engineering, Vol. Preprint. NO. Preprint, pp. 1–13. IOS Press (2015). doi:10.3233/ICA-150505
Bougoudis, I., Iliadis, L., Papaleonidas, A.: Fuzzy inference ANN ensembles for air pollutants modeling in a major urban area: the case of Athens. Eng. Appl. Neural Netw. Commun. Comput. Inf. Sci. 459, 1–14 (2014)
Fons, S., Achari, G., Ross, T.: A fuzzy cognitive mapping analysis of the impacts of an eco-industrial park. J. Intell. Fuzzy Syst. 15(2), 75–88 (2004)
Gordaliza, J.A., Florez, R.E.V.: Using fuzzy cognitive maps to support complex environmental issues learning. In: Proceedings of New Perspectives in Science Education Conference, 2nd edn. (2013)
Griffies, S.M.: Fundamentals of Ocean Models, p. 496. Princeton University Press, Princeton (2004)
Griffies, S.M., Gnanadesikan, A., Pacanowski, R., Larichev, V., Dukowicz, J.K., Smith, R.D.: Isopycnal mixing in a z-coordinate ocean model. J. Phys. Oceanogr. 28, 805–830 (1998)
Griffies, S.M.: Gent–McWilliams skew flux. J. Phys. Oceanogr. 28, 831–841 (1998)
Large, W., Danasbogulu, G., McWilliams, J., Gent, P., Bryan, F.O.: Equatorial circulation of a global ocean climate model with anisotropic viscosity. J. Phys. Oceanogr. 31, 518–536 (2001)
Lock, P., Brown, R., Bush, R., Martin, M., Smith, B.: A new boundary layer mixing scheme. Scheme description and single-column model tests. Mon. Weather Rev. 128, 3187–3199 (2000)
Luiz, J., Muller, E.: Greenhouse gas emission reduction under the kyoto protocol: the South African example. Int. Bus. Econ. Res. J. 7, 75–92 (2008)
Marco, F., Chalabi, Z., Foss, M.: Assessing framing assumptions in quantitative health impact assessments: a housing intervention example. Environ. Int. 59, 133–140 (2013)
Papageorgiou, E.I., Salmeron, J.L.: A review of fuzzy cognitive maps research during the last decade. IEEE Trans. Fuzzy Syst. 21(1), 66–79 (2013)
Paschalidou, A.: University of Ioannina, Ph.d. thesis development of box model for the air pollution forecasting in medium size cities (2007). (in Greek)
Pathinathan, T., Ponnivalavan, K.: The study of hazards of plastic pollution using induced fuzzy cognitive maps (IFCMS). J. Comput. Algorithm 3, 671–674 (2014)
Salmeron, J.L., Froelich, W.: Dynamic optimization of fuzzy cognitive maps for time series forecasting. Knowl. Based Syst. 105, 29–37 (2016). Forthcoming
Vidal, R., Salmeron, J.L., Mena, A., Chulvi, V.: Fuzzy cognitive map-based selection of TRIZ trends for eco-innovation of ceramic industry products. J. Cleaner Prod. 107, 202–214 (2015)
Winton, M.: A reformulated three-layer sea ice model. J. Atmos. Oceanic Technol. 17, 525–531 (2000)
Zhang, H., Song, J., Su, C., He, M.: Human attitudes in environmental management: fuzzy cognitive maps and policy option simulations analysis for a coal-mine ecosystem in China. J. Environ. Manag. 115, 227–234 (2013)
http://www.climatechange2013.org/images/report/WG1AR5_Chapter09_FINAL.pdf
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Anezakis, VD., Dermetzis, K., Iliadis, L., Spartalis, S. (2016). Fuzzy Cognitive Maps for Long-Term Prognosis of the Evolution of Atmospheric Pollution, Based on Climate Change Scenarios: The Case of Athens. In: Nguyen, NT., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9875. Springer, Cham. https://doi.org/10.1007/978-3-319-45243-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-45243-2_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45242-5
Online ISBN: 978-3-319-45243-2
eBook Packages: Computer ScienceComputer Science (R0)