DOI: 10.1016/j.atmosenv.2018.03.015
Scopus记录号: 2-s2.0-85043531553
论文题名: Prediction of hourly PM2.5 using a space-time support vector regression model
作者: Yang W ; , Deng M ; , Xu F ; , Wang H
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 181 起始页码: 12
结束页码: 19
语种: 英语
英文关键词: Gauss vector weight function
; Real-time air quality prediction
; Spatial clustering
; Spatial dependence
; Spatial heterogeneity
; Support vector regression
Scopus关键词: Air quality
; Artificial intelligence
; Autocorrelation
; Forecasting
; Learning systems
; Pollution
; Regression analysis
; Vectors
; Air quality prediction
; Spatial clustering
; Spatial dependence
; Spatial heterogeneity
; Support vector regression (SVR)
; Weight functions
; Vector spaces
; air quality
; autocorrelation
; Gaussian method
; heterogeneity
; machine learning
; model
; particulate matter
; pollutant
; prediction
; real time
; spatial analysis
; support vector machine
; Beijing [China]
; China
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: Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods. © 2018 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82905
Appears in Collections: 气候变化事实与影响
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作者单位: National-local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan, China; Department ofGeo-Informatics, Central South University, Changsha, China; China Railway First Survey and Design Institute Group Ltd., Xi'an, China
Recommended Citation:
Yang W,, Deng M,, Xu F,et al. Prediction of hourly PM2.5 using a space-time support vector regression model[J]. Atmospheric Environment,2018-01-01,181