FORECASTING THE AIR QUALITY USING OWA BASED TIME SERIES MODEL

摘要：

The environmental protection conception increasing, the prediction of air quality is more and more important The main Pollutant Standards Index (PSI) includes PM10, SO2, NO2, CO and O3 etc... The PSI will be produced and changed when combining in the air. Due to the concentrations of CO, SO2, NO2, and PM10 have declined, the focus of health studies and control efforts has increasingly turned to PM10 and O3 as the most important air pollutant species of concern. Correspondingly, the primary focus on the current understanding of the health is affected by PM10 and O3 in the Taiwan. Therefore, this study uses O3 attribute to evaluate air quality.This paper proposes an OWA based time series model to predict the air quality. Due to O3 data is belong to time series pattern, and OWA operator can aggregate multiple lag periods into single aggregated value by different situation parameters α. Based on the advantages of TSM and OWA, the OWA based time series model can efficiently and accurately predict PSI. In verification, this paper collects a practical data to verify the proposed method. The dataset contains records of 1061 days with O3 attribute from air qualities inspection station in Hsinchu city, Taiwan. From the results, the proposed method outperforms the listing methods.

关键词： Air Quality pollutant standards Indez time series method ordered weighted averaging

作者: SUE-FEN HUANG CHING-HSUE CHENG

作者单位: Information Management Department, University of National Yunlin Science and Technology, Yunlin, Taiwan

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3254-3259

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）