Abstract
The understanding and management of air quality problems is a suitable problem concerning the application of artificial intelligence (AI) methods towards knowledge discovery for the purposes of modelling and forecasting. As air quality has a direct impact on the quality of life and on the general environment, the ability to extract knowledge concerning relationships between parameters that influence environmental policy making and pollution abatement measures becoming more and more important. In the present paper an arsenal of AI methods consisting of Neural Networks and Principal Component Analysis is being supported by a set of mathematical tools including statistical analysis, fast Fourier transformations and periodograms, for the investigation and forecasting of photochemical pollutants time series in Athens, Greece. Results verify the ability of the methods to analyze and model this knowledge domain and to forecast the levels of key parameters that provide direct input to the environmental decision making process.
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Karatzas, K.D., Papadourakis, G., Kyriakidis, I. (2009). Understanding and Forecasting Air Pollution with the Aid of Artificial Intelligence Methods in Athens, Greece. In: Koutsojannis, C., Sirmakessis, S. (eds) Tools and Applications with Artificial Intelligence. Studies in Computational Intelligence, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88069-1_4
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DOI: https://doi.org/10.1007/978-3-540-88069-1_4
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