Forecasting Daily Ambient Air Pollution Based on Least Squares Support Vector Machines

摘要：

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 m ulti-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．

关键词： Least Squares Support Vector Machines Pollution Level Forecasting

作者: W.F.Ip C.M.Vong J.Y.Yang P.K.Wong

作者单位: Faculty of Science and Technology,University of Macau Macau,China

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-5

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）