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Forecasting daily ambient air pollution based on least squares support vector machines
Ip W.F.; Vong C.M.; Yang J.Y.; Wong P.K.
2010-08-24
Conference NameThe 2010 IEEE International Conference on Information and Automation
Source Publication2010 IEEE International Conference on Information and Automation, ICIA 2010
Pages571-575
Conference Date20-23 June 2010
Conference PlaceHarbin, China
Abstract

Meteorological and pollutions data are collected daily at monitoring stations of a city. This pollutant-related information can be used to build an early warning system, which provides forecast and also alarms health advice to local inhabitants by medical practicians and local government. In the literature, air quality or pollutant level predictive models using multi-layer perceptrons (MLP) have been employed at a variety of cities by environmental researchers. The practical applications of these models however suffer from different drawbacks so that good generalization may not be obtained. Least squares support vector machines (LS-SVM), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction. LS-SVM can overcome most of the drawbacks of MLP and has been reported to show promising results. ©2010 IEEE.

KeywordLeast Squares Support Vector Machines Pollution Level Forecasting
DOI10.1109/ICINFA.2010.5512401
URLView the original
Language英语
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
AffiliationUniversidade de Macau
Recommended Citation
GB/T 7714
Ip W.F.,Vong C.M.,Yang J.Y.,et al. Forecasting daily ambient air pollution based on least squares support vector machines[C],2010:571-575.
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