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- W977989739 abstract "The research activity described in this paper concerns the study of the phenomena responsible for the urban and suburban air pollution. The analysis carries on the work already developed by the NeMeFo (Neural Meteo Forecasting) research project for meteorological data short-term forecasting, Pasero (2004). The study analyzed the 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). 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. 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 the most widespread statistical data-learning techniques (Artificial Neural Networks, ANN, and Support Vector Machines, SVM)." @default.
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- W977989739 date "2007-01-01" @default.
- W977989739 modified "2023-09-27" @default.
- W977989739 title "A Machine Learning Tool to Forecast PM10 Level" @default.
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