摘要
大坝监控过程中,大坝变形的实测值是一个非线性且非平稳的时间序列,支持向量机(SVM)适用于解决小样本、非线性问题,在SVM算法的基础上建立了改进的大坝变形监控模型,利用差分自回归移动平均模型(ARIMA)解决非平稳时间序列问题的优势,对SVM模型的残差进行处理,并采用粒子群算法(PSO)优化支持向量机(SVM)中的核函数。实例分析表明,优化后的组合模型预测结果可靠,且精度较SVM模型有所提高。
The monitoring data of dam deformation is a nonlinear and non-stationary time series. The Support Vector Machine（ SVM） is suitable for solving the problems of small sample and nonlinear effectively,so dam deformation model is established on the basis of SVM and came up with improved methods. With the aim of solving the non-stationary issue effectively,the paper used ARIMA model of SVM model fitting to predict the residual item correction,then used Particle Swarm Optimization（ PSO） to improve the accuracy of nuclear parameter optimization of SVM. Using the measured distortion monitoring data of a domestic dam as an example,the model is tested to be of high precision and credible results and it is worthy of being applied to the dam deformation forecast or other hydropower projects.
作者
黄梦婧
杨海浪
HUANG Mengjing;YANG Hailang(College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China;State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering,Hohai University,Nanjing 210098;Anhui Water Conservancy Technical College,Hefei 231603,China;Anhui Provincial Yangtze River-to-Huaihe River Water Diversion,Hetei 230601,China)
出处
《人民黄河》
CAS
北大核心
2018年第8期149-151,156,共4页
Yellow River
基金
国家自然科学基金重点项目(51139001
51179066)
高等学校博士学科点专项科研基金资助项目(20130094110010)
