Prediction of air pollutant levels using support vector machines: An effective tool

W. Lu; W. Wang; X. Wang; A. Y T Leung

Prediction of air pollutant levels using support vector machines: An effective tool

W. Lu
, W. Wang
, X. Wang
, A. Y T Leung

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

Abstract

Forecasting of air quality parameters is an important topic of atmospheric and environmental research today due to the health impact caused by airborne pollutants existing in urban areas. The support vector machine (SVM), as a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented here aims to examine the feasibility of applying SVM to predict pollutant concentrations. In the meantime, the functional characteristics of the SVM are also investigated in the study. The experimental comparison between the SVM and the classical radial basis function (RBF) network demonstrates that the SVM is superior to conventional RBF in predicting air quality parameters with different time series. © 2003, Civil-Comp Ltd.

Original language	English
Title of host publication	Proceedings of the 7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, AICivil-Comp 2003
Publisher	Civil-Comp Press
Volume	78
ISBN (Print)	094874992, 9780948749926
Publication status	Published - 2003
Externally published	Yes
Event	7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, AICivil-Comp 2003 - Egmond-aan-Zee, Netherlands Duration: 2 Sept 2003 → 4 Sept 2003

Publication series

Name	Civil-Comp Proceedings
Volume	78
ISSN (Print)	1759-3433

Conference

Conference	7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, AICivil-Comp 2003
Place	Netherlands
City	Egmond-aan-Zee
Period	2/09/03 → 4/09/03

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities

Research Keywords

Air pollutant
Artificial neural network
Forecast
Radial basis function
Support vector machine
Time series

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

@inproceedings{cad5906674ed4ec784339d1adc48d935,

title = "Prediction of air pollutant levels using support vector machines: An effective tool",

abstract = "Forecasting of air quality parameters is an important topic of atmospheric and environmental research today due to the health impact caused by airborne pollutants existing in urban areas. The support vector machine (SVM), as a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented here aims to examine the feasibility of applying SVM to predict pollutant concentrations. In the meantime, the functional characteristics of the SVM are also investigated in the study. The experimental comparison between the SVM and the classical radial basis function (RBF) network demonstrates that the SVM is superior to conventional RBF in predicting air quality parameters with different time series. {\textcopyright} 2003, Civil-Comp Ltd.",

keywords = "Air pollutant, Artificial neural network, Forecast, Radial basis function, Support vector machine, Time series",

author = "W. Lu and W. Wang and X. Wang and Leung, \{A. Y T\}",

year = "2003",

language = "English",

isbn = "094874992",

volume = "78",

series = "Civil-Comp Proceedings",

publisher = "Civil-Comp Press",

booktitle = "Proceedings of the 7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, AICivil-Comp 2003",

address = "United Kingdom",

note = "7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, AICivil-Comp 2003 ; Conference date: 02-09-2003 Through 04-09-2003",

}

Lu, W, Wang, W, Wang, X & Leung, AYT 2003, Prediction of air pollutant levels using support vector machines: An effective tool. in Proceedings of the 7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, AICivil-Comp 2003. vol. 78, Civil-Comp Proceedings, vol. 78, Civil-Comp Press, 7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, AICivil-Comp 2003, Egmond-aan-Zee, Netherlands, 2/09/03.

Prediction of air pollutant levels using support vector machines: An effective tool. / Lu, W.; Wang, W.; Wang, X. et al.
Proceedings of the 7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, AICivil-Comp 2003. Vol. 78 Civil-Comp Press, 2003. (Civil-Comp Proceedings; Vol. 78).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Prediction of air pollutant levels using support vector machines

T2 - 7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, AICivil-Comp 2003

AU - Lu, W.

AU - Wang, W.

AU - Wang, X.

AU - Leung, A. Y T

PY - 2003

Y1 - 2003

N2 - Forecasting of air quality parameters is an important topic of atmospheric and environmental research today due to the health impact caused by airborne pollutants existing in urban areas. The support vector machine (SVM), as a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented here aims to examine the feasibility of applying SVM to predict pollutant concentrations. In the meantime, the functional characteristics of the SVM are also investigated in the study. The experimental comparison between the SVM and the classical radial basis function (RBF) network demonstrates that the SVM is superior to conventional RBF in predicting air quality parameters with different time series. © 2003, Civil-Comp Ltd.

AB - Forecasting of air quality parameters is an important topic of atmospheric and environmental research today due to the health impact caused by airborne pollutants existing in urban areas. The support vector machine (SVM), as a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented here aims to examine the feasibility of applying SVM to predict pollutant concentrations. In the meantime, the functional characteristics of the SVM are also investigated in the study. The experimental comparison between the SVM and the classical radial basis function (RBF) network demonstrates that the SVM is superior to conventional RBF in predicting air quality parameters with different time series. © 2003, Civil-Comp Ltd.

KW - Air pollutant

KW - Artificial neural network

KW - Forecast

KW - Radial basis function

KW - Support vector machine

KW - Time series

UR - https://www.scopus.com/pages/publications/84893858485

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84893858485&origin=recordpage

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 094874992

SN - 9780948749926

VL - 78

T3 - Civil-Comp Proceedings

BT - Proceedings of the 7th International Conference on the Application of Artificial Intelligence to Civil and Structural Engineering, AICivil-Comp 2003

PB - Civil-Comp Press

Y2 - 2 September 2003 through 4 September 2003

ER -

Prediction of air pollutant levels using support vector machines: An effective tool

Abstract

Publication series

Conference

UN SDGs

Research Keywords

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this