Predicting QoL parameters for the atmospheric environment in Athens, Greece

Abstract

Air quality has a direct impact on the quality of life and on the general environment. Understanding and managing urban air quality is a suitable problem domain for the application of artificial intelligence (AI) methods towards knowledge discovery for the purposes of modeling and forecasting. In the present paper Artificial Neural Networks are supplemented by a set of mathematical tools including statistical analysis and Fast Fourier Transformations for the investigation and forecasting of hourly benzene concentrations and highest daily 8 hour mean of (8-HRA) ozone concentrations for two locations in Athens, Greece. The methodology is tested for its forecasting ability. Results verify the approach that has been applied, and the ability to analyze and model the specific knowledge domain and to forecast key parameters that provide direct input to the environmental decision making process. © 2010 Springer-Verlag Berlin Heidelberg.