A Simple Feedforward Neural Network for the PM 10 Forecasting: Comparison with a Radial Basis Function Network and a Multivariate Linear Regression Model M. Caselli & L. Trizio & G. de Gennaro & P. Ielpo Received: 30 June 2008 / Accepted: 1 December 2008 / Published online: 23 December 2008 # Springer Science + Business Media B.V. 2008 Abstract The problem of air pollution is a frequently recurring situation and its management has social and economic considerable effects. Given the interaction of the numerous factors involved in the raising of the atmospheric pollution rates, it should be considered that the relation between the intensity of emission produced by the polluting source and the resulting pollution is not immediate. The aim of this study was to realise and to compare two support decision system (neural networks and multivariate regression model) that, correlating the air quality data with the meteorological information, are able to predict the critical pollution events. The development of a back-propagation neural network is presented to predict the daily PM 10 concentration 1, 2 and 3 days early. The measurements obtained by the territorial monitoring stations are one of the primary data sources; the forecasting of the major weather parameters available on the website and the forecasting of the Saharan dust obtained by the “Centro Nacional de Supercomputaciòn” website, satellite images and back trajectories analysis are used for the weather input data. The results obtained with the neural network were compared with those obtained by a multivariate linear regression model for 1 and 2 days forecasting. The relative root mean square error for both methods shows that the artificial neural networks (ANN) gives more accurate results than the multivariate linear regression model mostly for 1 day forecasting; moreover, the regression model used, in spite of ANN, failed when it had to fit spiked high values of PM 10 concentration. Keywords PM 10 . Forecast . Neural network . Multivariate linear regression 1 Introduction The air we breathe everyday can be contaminated by polluting substances emitted by industries, vehicles, or other sources. These polluting substan- ces can have bad effects both on human health and on the environment. One of the most dangerous pollutants is PM 10 , which is particulate matter having an average aero- dynamic diameter smaller than 10 μm. Health effects range from minor effects, such as nose and throat irritation, to more serious effects such as aggravation of existing respiratory and cardiovascular disease, increased hospital admis- sions and premature death (Dockery et al. 1989, 1993; Gamble 1998; IARC 1987; Oberdorster 2001; Slaughter et al. 2005). Air pollution control is necessary to prevent the situation from worsening in the long run. On the other hand, short-term forecasting of air quality is needed in Water Air Soil Pollut (2009) 201:365–377 DOI 10.1007/s11270-008-9950-2 M. Caselli : L. Trizio (*) : G. de Gennaro : P. Ielpo Department of Chemistry, University of Bari, Via E. Orabona 4, 70126 Bari, Italy e-mail: liviatrizio@lenviros.it