Neural Networks - A New Mathematical Tool ForAir Pollution Modelling

Abstract

Modelling is a basis for better understanding and prevention of air pollution. Many different mathematical techniques have been used in this field. In this paper a new tool for air pollution modelling neural networks will be described. Its capabilities and disadvantages will be shown in the cases of wind forecasting and reconstruction, and SC>2 pollution forecasting. INTRODUCTION Air pollution is one of the biggest problems of industrialised areas. In order to better understand this phenomena, several different modelling techniques have been applied in this field that are very well known. Research in mathematical fields and in many other modern fields of science also gave some rather new tools that can be usefully employed in the field of air pollution modelling. One such tool, that so far was very rarely used in air pollution modelling, is that of neural networks, which are widely used in other fields such as pattern recognition, speech recognition and synthesis, financial forecasting, etc. The performance of neural network based models will be shown with examples of wind reconstruction and SO2 pollution forecasting in the Sostanj basin (complex orography) in Slovenia. The Sostanj Thermal Power Plant Transactions on Ecology and the Environment vol 6, © 1995 WIT Press, www.witpress.com, ISSN 1743-3541