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《Science of Surveying and Mapping》 2016-01
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Real time prediction of PM_(2.5)concentration based on support vector regression algorithms

ZHU Yajie;LI Qi;HOU Junxiong;FENG Xiao;FAN Junxiang;Institute of Remote Sensing and Geographic Information System,Beijing University;  
In order to develop a real time air quality prediction model suitable for severe pollution condition of China,the paper used the support vector regression algorithm to analyze the surface air quality monitoring data and meteorological data of Beijing city,and then presented an real time prediction model of PM2.5concentration.Experimental result showed that the model could forecast daily PM2.5Concentration within six days and hourly PM2.5 Concentration within 72 hours with high speeds of both model training and model predicting,which makes it a favorable method to build a real time forecasting system of PM2.5Concentration.
【Fund】: 国家科技支撑计划项目(2012BAC20B06)
【CateGory Index】: X513
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