A neural architecture to predict pollution in industrial areas

In this paper a novel approach, based on a neural network structure, is introduced in order to face the problem of pollutant estimation in an industrial area. In particular a short-term prediction (six hours ahead) of the SO₂ pollutant mean value has been performed. A neural architecture, based essentially on a suitable number of MLPs devoted to predict alarm situations and to estimate the mean value of the pollutant, has been implemented. The strategy employed has been revealed to be particularly suitable, as it is shown in the results reported in the paper