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Erschienen in:
2007 | OriginalPaper | Buchkapitel
An Application of Machine Learning Methods to PM10 Level Medium-Term Prediction
verfasst von : Giovanni Raimondo, Alfonso Montuori, Walter Moniaci, Eros Pasero, Esben Almkvist
Erschienen in: Knowledge-Based Intelligent Information and Engineering Systems
Verlag: Springer Berlin Heidelberg
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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 PM
10
). 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 wide-spread statistical data driven techniques (Artificial Neural Networks, ANN, and Support Vector Machines, SVM).