基于KPCA-NGO-LSSVM的混凝土坝变形预测模型
Deformation Prediction Method of Concrete Dam Based on KPCA-NGO-LSSVM
詹明强 1陈波 2袁志颖3
作者信息
- 1. 福建水口发电集团有限公司,福建 福州 350001
- 2. 河海大学水利水电学院,江苏 南京 210098
- 3. 中国电建集团中南勘测设计研究院有限公司,湖南 长沙 410014
- 折叠
摘要
变形作为最直观的监测指标,常用来反映大坝的服役性态变化.为建立更加符合混凝土坝变形的预测模型,实现更高精度的混凝土坝变形预测,针对混凝土坝变形序列呈现不确定性和非线性的特征,将核主成分分析(KPCA)引入最小二乘支持向量机(LSSVM)来约简因子关系,降低预测模型的输入维数和复杂度,同时使用北方苍鹰优化算法(NGO)对最小二乘支持向量机进行参数寻优,构建了基于KPCA-NGO-LSSVM的混凝土坝变形预测模型.工程实例表明,KPCA-NGO-LSSVM模型相比传统多元线性回归(MLR)、LSSVM、KPCA-LSSVM的预测值与实际值的拟合效果更好,预测精度更高,能更有效地预测混凝土坝变形.
Abstract
As the most intuitive monitoring index,deformation is often used to reflect the change of the service be-havior of the dam.In order to establish a prediction model which is more in line with the deformation of concrete dam and realize more accurate prediction of dam deformation,aiming at the uncertain and nonlinear characteristics of deformation sequence of concrete dam,kernel principal component analysis(KPCA)is introduced into least square support vector ma-chine(LSSVM)to reduce the factor relationship and reduce the input dimension and complexity of the prediction model.At the same time,the northern goshawk optimization algorithm(NGO)is used to optimize the parameters of the least square support vector machine,and the concrete dam deformation prediction model based on KPCA-NGO-LSSVM is con-structed.The engineering example shows that the fitting effect between the predicted value and the actual value of KPCA-NGO-LSSVM model is better than that of traditional multiple linear regression(MLR),LSSVM and KPCA-LSSVM,and the prediction accuracy is higher,which can be used to predict the deformation of concrete dam more effectively.
关键词
混凝土坝/核主成分分析/北方苍鹰算法/最小二乘支持向量机/变形预测Key words
concrete dam/kernel principal component analysis/northern goshawk optimization algorithm/least square support vector machine/deformation prediction引用本文复制引用
基金项目
国家自然科学基金项目(52079049)
国家自然科学基金项目(52239009)
国家重点实验室基本科研业务费项目(522012272)
出版年
2024
NETL
NSTL
万方数据