摘要
为解决土石坝渗流监测数据的分析问题,采用基于主成分分析的支持向量机法,对某均质坝体内渗流浸润线的监测数据建立了预测模型,并针对坝体内的测压管水位进行了具体计算预测。结果表明,主成分分析法可以有效降维,并较好的综合反映坝体内测压管水位的主要影响因素;预测值与实测值的误差分析结果表明,支持向量机模型在小样本坝体监测数据分析预测方面的精度较高,该模型可为其他类似工程监测数据的分析预测提供新的方法。
Adopting the principal component analysis based support vector machine method, a predictive analysis model of dam monitoring is established, and applied to dam seepage monitoring. The piezometric level in a dam is also forecasted by this model.The comparison of measured results with predicted results shows that the principal component analysis method can decrease the dimensions of measured result effectively, and better reflects major effect factors of piezometric level in dam. Error analysis of the model shows that the support vector machine method has higher forecast precision in small samples, it provides a new way of forecasting seepage monitoring of dam, and the model offers a useful reference for other similar project as well.
作者
李瑞光
臧国轻
Li Ruiguang;Zang Guoqing(Foreign Language Teaching Department of University,Henan University,Kaifeng,Henan 475001,China;Henan University)
出处
《计算机时代》
2018年第6期5-8,共4页
Computer Era
基金
国家自然科学基金项目(61374134)
关键词
支持向量机
坝体监测
主成分分析
预测模型
support vector machine
dam monitoring
principal component analysis
predictive model
