期刊
ATMOSPHERIC ENVIRONMENT
卷 181, 期 -, 页码 12-19出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.atmosenv.2018.03.015
关键词
Real-time air quality prediction; Spatial heterogeneity; Spatial dependence; Support vector regression; Spatial clustering; Gauss vector weight function
资金
- National Natural Science Foundation of China [41730105]
- National Key Research and Development Foundation of China [2016YFB050230001]
- Hunan Provincial Natural Science Foundation of China [2018JJ3150]
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.
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