Artificial Neural Networks for Pollution Forecast

This chapter provides an introduction to non-linear methods for the prediction of the concentration of air pollutants. We focused on the selection of features and the modelling and processing techniques based on the theory of Artificial Neural Networks, using Multi Layer Perceptrons and Support Vector Machines. Joint measurements of meteorological data and pollutants concentrations is useful in order to increase the number of parameters to be studied for the construction of mathematical air quality forecasting models and hence to improve forecast performances. Weather variables have a non-linear relationship with air quality, which can be captured by non-linear models such as Multi Layer Perceptrons and Support Vector Machines. Our analysis carries on the work already developed by the NeMeFo (Neural Meteo Forecasting) research project for meteorological data short-term forecasting (Pasero et al., 2004). The application provided in Section 4 illustrates how the theoretical methods for feature selection (Section 2) and data modelling (Section 3) can be implemented for the solution of a specific problem of air pollution forecast. The principal causes of air pollution are identified and the best subset of features (meteorological data and air pollutants concentrations) for each air pollutant is selected in order to predict its medium-term concentration (in particular for the PM10). The selection of the best subset of features was implemented by means of a backward selection algorithm which is based on the information theory notion of relative entropy. Multi Layer Perceptrons and Support Vector Machines constitute some of the most wide-spread statistical data-learning techniques to develop data-driven models. Their use is shown for the prediction problem considered. In conclusion, the final aim of this research is the implementation of a prognostic tool able to reduce the risk for the air pollutants concentrations to be above the alarm thresholds fixed by the law. The detection of meteorological and air pollutant data, the automatic selection of optimal descriptors of such data and the use of Multi Layer Perceptrons and Support Vector Machines are proposed as an efficient strategy to perform an accurate prediction of the time evolution of air pollutant concentration.