Monitoring and early warning of air quality based on
improved support vector machines
1. School of Public Administration, Dongbei University of Finance and Economics, Dalian, Liaoning 116025, China; 2. School of International Business, Dongbei University of Finance and Economics, Dalian, Liaoning 116025, China; 3. School of Statistics, Dongbei University of Finance and Economics, Dalian, Liaoning 116025, China
Abstract：Using particle swarm optimization and combination forecast to improve the accuracy and effectiveness of the calculation method of traditional support vector machine, the monitoring and early warning model of urban air quality was established based on the data of air quality index(AQI). In order to improve the selection precision of penalty parameters and nuclear parameters, the procedure of parameters selection of grid search and cross validation method was optimized by particle swarm optimization with convergence factor. To refine the model, the combination forecast was used to realize optimal linear combination of respective prediction results of Grey prediction, time series prediction and PSOSVM model. The results show that the improved parameter selection process and the monitoring and early warning of air quality based on support vector machines have the characteristics of low risk prediction data structure, minimum prediction mean square error, high accuracy, quick calculation speed and wide application.
胡世前, 姜倩雯, 凌冰, 尹伟东. 基于改进支持向量机的空气质量监测预警模型[J]. 江苏大学学报（自然科学版）, 2016, 37(4): 491-496.
HU Shi-Qian, Jiang-Qian-Wen, Ling-Bing, Yin-Wei-Dong. Monitoring and early warning of air quality based on
improved support vector machines[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2016, 37(4): 491-496.
2016, Vol. 37 
