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.
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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
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