Author(s)

M. Cossentino, A. Damiani, S. Gaglio, F.M. Raimondi & M.C. Vitale

Abstract

Exploring the use of soft-computing and artificial intelligence techniques in atmospheric pollution modelling M. cossentino1, A. ~arniani', S. ~ a ~ l i o ' , F.M. ~airnondi~ & M.C. vitale2 '~stituto di Calcolo e Reti ad -4lte Prestazioni, Consiglio Nazionale delle Ricerche, Italy 2~ipartimento di Ingegneria dell Yutomazione e dei Sistemi, University of Palerrno, Italy Abstract Many different mathematical models for monitoring and controlling the atmospheric pollution have been applied in recent years. In this paper we illustrate a method for the forecast of atmospheric pollution caused by particulate matter (PMlo). This model has been developed using Bayesian networks to represent the pollutant behaviour and then the parameters of these networks have been optimized using genetic algorithms. We adopted this approach because the probabilistic nature of the Bayesian models well reflects the uncertainty characterizing the atmospheric pollution data and meteorological parameters. Several experim

Keywords