Forecasting air quality parameters using hybrid neural network modelling

Urban air pollution has emerged as an acute problem in recent years because of its detrimental effects on health and living conditions. The research p resented here aims at attaining a better understanding of phenomena associa ted with atmospheric pollution, and in particular with aerosol particles. T he specific goal was to develop a form of air quality modelling which can f orecast urban air quality for the next day using airborne pollutant, meteor ological and timing variables. Hourly airborne pollutant and meteorological averages collected during the years 1995-1997 were analysed in order to identify air quality episodes hav ing typical and the most probable combinations of air pollutant and meteoro logical variables. This modelling was done using the Self-Organising Map (S OM) algorithm, Sammon's mapping and fuzzy distance metrics. The clusters of data that were found were characterised by statistics. Several overlapping Multi-Layer Perceptron (MLP) models were then applied to the clustered dat a, each of which represented one pollution episode. The actual levels for i ndividual pollutants could then be calculated using a combination of the ML P models which were appropriate in that situation. The analysis phase of the modelling gave clear and intuitive results regard ing air quality in the area where the data had been collected. The resultin g forecast showed that the modelling of gaseous pollutants is more reliable than that of the particles.