Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines
Joint Authors
Wong, Pak-kin
Ip, Weng-Fai
Vong, Chi Man
Yang, Jing-yi
Source
Journal of Control Science and Engineering
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-06-25
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Electronic engineering
Information Technology and Computer Science
Abstract EN
Forecasting of air pollution is a popular and important topic in recent years due to the health impact caused by air pollution.
It is necessary to build an early warning system, which provides forecast and also alerts health alarm to local inhabitants by medical practitioners and the local government.
Meteorological and pollutions data collected daily at monitoring stations of Macau can be used in this study to build a forecasting system.
Support vector machines (SVMs), a novel type of machine learning technique based on statistical learning theory, can be used for regression and time series prediction.
SVM is capable of good generalization while the performance of the SVM model is often hinged on the appropriate choice of the kernel.
American Psychological Association (APA)
Vong, Chi Man& Ip, Weng-Fai& Wong, Pak-kin& Yang, Jing-yi. 2012. Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines. Journal of Control Science and Engineering،Vol. 2012, no. 2012, pp.1-11.
https://search.emarefa.net/detail/BIM-478038
Modern Language Association (MLA)
Vong, Chi Man…[et al.]. Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines. Journal of Control Science and Engineering No. 2012 (2012), pp.1-11.
https://search.emarefa.net/detail/BIM-478038
American Medical Association (AMA)
Vong, Chi Man& Ip, Weng-Fai& Wong, Pak-kin& Yang, Jing-yi. Short-Term Prediction of Air Pollution in Macau Using Support Vector Machines. Journal of Control Science and Engineering. 2012. Vol. 2012, no. 2012, pp.1-11.
https://search.emarefa.net/detail/BIM-478038
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-478038