DOI: 10.1016/j.atmosenv.2014.09.046
Scopus记录号: 2-s2.0-84907857975
论文题名: Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models
作者: Qin S ; , Liu F ; , Wang J ; , Sun B
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 98 起始页码: 665
结束页码: 675
语种: 英语
英文关键词: Cuckoo search
; Ensemble empirical model decomposition
; Gray correlation
; Hybrid model
; Particulate matter (PM)
Scopus关键词: Cuckoo searches
; Empirical model decomposition
; Gray correlation
; Hybrid model
; Particulate Matter
; atmospheric pollution
; concentration (composition)
; correlation
; early warning system
; emission control
; ensemble forecasting
; particulate matter
; pollutant source
; terrain
; air pollutant
; Article
; back propagation
; China
; controlled study
; correlation analysis
; decomposition
; forecasting
; humidity
; particulate matter
; simulation
; temperature
; Beijing [China]
; China
; Gansu
; Guangdong
; Guangzhou
; Lanzhou
; Shanghai
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: The analysis and forecasting of PM concentrations play a significant role in regulatory planning on the reduction and control of PM emission and precautionary strategies. However, accurate PM forecasting, which is needed to establish an early warning system, is still a huge challenge and a critical issue. Determining how to address the accurate forecasting problem becomes an even more significant and urgent task. Based on gray correlation analysis (GCA), Ensemble Empirical Mode Decomposition (EEMD), Cuckoo search (CS) and Back-propagation artificial neutral networks (BPANN), this paper proposes the CS-EEMD-BPANN model for forecasting PM concentrations. Prior to establishing this model, gray correlation has been uniquely used to search for possible predictors of PM among other air pollutants (CO, NO2, O3 and SO2) and meteorological environments (wind speed, wind direction, temperature, humidity and pressure). The proposed method was investigated in four major cities of China (Beijing, Shanghai, Guangzhou and Lanzhou) with different characteristics of climatic, terrain and emission sources. The results of the gray correlation analysis indicate that CO, NO2 and SO2 are more related to PM and that the incorporation of these predictors can significantly improve the model performance predictability, suggesting the effectiveness of our developed method. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80552
Appears in Collections: 气候变化事实与影响
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作者单位: MOE Key Laboratory of Western China's Environmental Systems, Research School of Arid Environment and oClimate Change, Lanzhou University, Lanzhou, China; School of Mathematics and Statistics, Lanzhou University, Lanzhou, China; School of Statistics, Dongbei University of Finance and Economics, Dalian, China; China Water Resources Beifang Investigation Design and Research Co. Ltd, Tianjin, China
Recommended Citation:
Qin S,, Liu F,, Wang J,et al. Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models[J]. Atmospheric Environment,2014-01-01,98