An Application of Machine Learning Methods to PM10 Level Medium-Term Prediction

The study described in this paper, analyzed the urban and suburban air pollution principal causes and identified the best subset of features (meteorological data and air pollutants concentrations) for each air pollutant in order to predict its medium-term concentration (in particular for the PM10). An information theoretic approach to feature selection has been applied in order to determine the best subset of features by means of a proper backward selection algorithm. The final aim of the 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 implementation of this tool will be carried out using machine learning methods based on some of the most widespread statistical data driven techniques (Artificial Neural Networks, ANN, and Support Vector Machines, SVM).

• Type: Journal
• Year: 2007
• Where: KES
• Authors: Giovanni Raimondo, Alfonso Montuori, Walter Moniaci, Eros Pasero, Esben Almkvist
• Keywords: Air Pollutants Concentrations, Information Technology, KES 2007, Pollutants, Suburban Air Pollution