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Air Quality Assessment by Neural Networks
Abstract
The chapter presents an overview of current methods for air quality assessment, i.e. air stress indices and air quality indices. Traditional air quality assessment is realized using air quality indices which are determined as mean values of selected air pollutants. Thus, air quality assessment depends on strictly given limits without taking into account specific local conditions and synergic relations between air pollutants and other meteorological factors. The stated limitations can be eliminated, e.g. using systems based on neural networks and fuzzy logic. Therefore, the chapter presents a design of a model for air quality assessment based on a combination of Kohonen’s self-organizing feature maps and fuzzy logic neural networks. The model makes it possible to analyze the structure of data, to find localities with similar air quality, and to interpret the classification results by means of fuzzy logic. Due to its generalization ability, it is also possible to classify unknown localities into classes assessing their air quality.
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