Prediction of hourly PM 2.5 using a space-time support vector regression model,Atmospheric Environment

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Prediction of hourly PM 2.5 using a space-time support vector regression model
Atmospheric Environment ( IF 4.2 ) Pub Date : 2018-05-01 , DOI: 10.1016/j.atmosenv.2018.03.015
Wentao Yang , Min Deng , Feng Xu , Hang Wang

Abstract 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.

更新日期：2018-05-01

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