Hybrid Soft Computing for Atmospheric Pollution-Climate Change Data Mining

Prolonged climate change contributes to an increase in the local concentrations of O3 and PMx in the atmosphere, influencing the seasonality and duration of air pollution incidents. Air pollution in modern urban centers such as Athens has a significant impact on human activities such as industry and transport. During recent years the economic crisis has led to the burning of timber products for domestic heating, which adds to the burden of the atmosphere with dangerous pollutants. In addition, the topography of an area in conjunction with the recording of meteorological conditions conducive to atmospheric pollution, act as catalytic factors in increasing the concentrations of primary or secondary pollutants. This paper introduces an innovative hybrid system of predicting air pollutant values (IHAP) using Soft computing techniques. Specifically, Self-Organizing Maps are used to extract hidden knowledge in the raw data of atmospheric recordings and Fuzzy Cognitive Maps are employed to study the conditions and to analyze the factors associated with the problem. The system also forecasts future air pollutant values and their risk level for the urban environment, based on the temperature and rainfall variation as derived from sixteen CMIP5 climate models for the period 2020–2099.

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