2010 | OriginalPaper | Buchkapitel
A Feature Selection Method for Air Quality Forecasting
verfasst von : Luca Mesin, Fiammetta Orione, Riccardo Taormina, Eros Pasero
Erschienen in: Artificial Neural Networks – ICANN 2010
Verlag: Springer Berlin Heidelberg
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Local air quality forecasting can be made on the basis of meteorological and air pollution time series. Such data contain redundant information. Partial mutual information criterion is used to select the regressors which carry the maximal non redundant information to be used to build a prediction model. An application is shown regarding the forecast of PM
10
concentration with one day of advance, based on the selected features feeding an artificial neural network.