摘要
Abstract
Using the observational data from January 2000 to March 2010 at Urumqi International Airport, this paper constructed a sample space which include six forecasting objects： hourly visibility, temperature, and weather phenomena, and daily maximum temperature, minimum temperature, and precipitation, and employed the SVM method to do cross-validation and prediction modeling. The results showed that the prediction model kept good stability, and had better prediction effect for above objects.关键词
支持向量机（SVM）/分类预测/回归预测/温度/能见度/降水量Key words
support vector machine （SVM）/classification forecast/regression forecast/temperature/visibility/precipitation分类
天文与地球科学
