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)

An Application of Machine Learning Methods to PM10 Level Medium-Term Prediction / Raimondo, Giovanni; Montuori, A; Moniaci, Walter; Pasero, EROS GIAN ALESSANDRO; Almkvist, E.. - STAMPA. - Volume 4694:(2007), pp. 259-266. (Intervento presentato al convegno International Conference on Knowledge-Based and Intelligent Information & Eng. Systs (KES) 2007 tenutosi a VIETRI SUL MARE (SA), ITALY nel 12-14 SEPTEMBER 2007, .).

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

RAIMONDO, Giovanni;MONIACI, WALTER;PASERO, EROS GIAN ALESSANDRO;
2007

Abstract

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)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1954658
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