VISIBILITY FORECAST BASED ON PROXIMAL SUPPORT VECTOR MACHINE
WU Bo
HU Bang-hui
WANG Xue-zhong
HUANG Hong
WANG Ju
Abstract：Based on Ts11L61 numerical prediction products and observed stations data of December and January from 2008 to 2010,a classification-regression forecast model is established for visibility at Nanjing,Hangzhou and Quzhou stations by using proximal support vector machine.The model is tested using independent samples of December and January in 2011,and compared with the regression one.The results indicate that the forecasting effect of the classification-regression model is better than that of the regression one.The average accuracy of classification-regression model for the three stations in 24 h,36 h,48 h,60 h and 72 h is 75.5％,83.7％,72.1％,75.4％ and 78.0％,respectively.Its average accuracy is also higher than that of regression one.The classification-regression model is suitable for forecasting visibility at these stations.
Keywords：proximal support vector machineclassification-regression modelvisibilityforecast
Publication Date：2017-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 104-110 )