Neural networks for sulphur dioxide ground level concentrations forecasting
In this paper we present the preliminary results on the use of neural networks to forecast SO2 concentration levels in the industrial area of Ravenna. Ground level concentrations of pollutants were analysed in the area of Ravenna, in particular the high levels of SO2 occurring during relatively rare episodes. These events are typically correlated with many different aspects, like complex local meteorology, topography, and industrial emissions parameters. In many cases, during these episodes, the deterministic models (e.g. Gaussian models) fail to explain the high ground level concentrations. The neural networks are trained with a Bayesian learning scheme.
bayesian learning air pollution arx models multi-layer perceptrons