摘要
支持向量机是基于统计学习理论的新一代机器学习技术,其非线性回归预测性能优越于传统统计方法。利用前一天该污染物的日均浓度、前一天地面平均风速等7个预报因子建立了基于RBF核函数支持向量回归法的大气污染预报模型,并利用十重交叉验证和网格搜索法寻找模型最优参数。乌鲁木齐大气预报实例表明:支持向量机显示出小样本时预报精度较高和训练速度快的独特优势,为空气质量预报提供一种全新的模式。
The support vector machine （SVM）, a new generation machinery learning technology based on statistical theory, has been reported to have better prediction performance of non-liner regression than traditional statistical methods. An SVM regression （SVMR） model for atmospheric pollution prediction is developed according to seven forecast factors, including the daily average pollutant concentration of previous day, daily average wind speed of previous day, etc. Meanwhile, 10-fold cross-validation and grid-search methods are applied to find the best parameters of SVMR. The experimental results of Urumqi data show that SVM has the unique advantage of high prediction accuracy and training rate on small-size data sets. It suggests a new model for prediction of atmospheric pollution.
出处
《气象》
CSCD
北大核心
2006年第12期61-65,共5页
Meteorological Monthly
关键词
大气污染预报
支持向量机(SVM)
交叉验证
网格搜索
atmospheric pollution prediction support vector machine （SVM） cross-validation grid-search
