A feature selection method for air quality forecasting

Authors:
Luca Mesin;Fiammetta Orione;Riccardo Taormina;Eros Pasero
Affiliations:
Department of Electronics, Politecnico di Torino, Torino, Italy;Department of Electronics, Politecnico di Torino, Torino, Italy;Department of Electronics, Politecnico di Torino, Torino, Italy;Department of Electronics, Politecnico di Torino, Torino, Italy
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

Citing 5
Cited 0

Elements of information theory

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Non-linear variable selection for artificial neural networks using partial mutual information

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PM10 forecasting for Thessaloniki, Greece

Environmental Modelling & Software

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Abstract

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 PM10 concentration with one day of advance, based on the selected features feeding an artificial neural network.