基于KPCA-ICFSFDP-MOGP的拱坝多测点变形数据预处理及预测方法  

Preprocessing and prediction method for multi-point deformation data of arch dams based on KPCA-ICFSFDP-MOGP

作　　者：葛盼猛 陈波[1,2] 刘庭赫 杨帆 GE Panmeng;CHEN Bo;LIU Tinghe;YANG Fan(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,China;China Water Northeastern Investigation,Design&Research Co.,Ltd.,Changchun 130021,China;China Construction Metal Structure Association,Beijing 100037,China)

机构地区：[1]河海大学水利水电学院,江苏南京210098 [2]河海大学水文水资源与水利工程科学国家重点实验室,江苏南京210098 [3]中水东北勘测设计研究有限责任公司,吉林长春130021 [4]中国建筑金属结构协会,北京100037

出　　处：《水利水电科技进展》2023年第4期92-97,共6页Advances in Science and Technology of Water Resources

基　　金：国家自然科学基金面上项目(52079049);江苏省基础研究计划青年项目(BK20160872)。

摘　　要：为更高效地对高拱坝的多测点变形数据进行分析,引入了核主成分分析(KPCA)算法重构误差以识别多测点序列中的异常值;为解决用于测点聚类的密度峰值聚类(CFSFDP)算法高维表现较差的问题,利用KPCA算法对其进行降维操作,并提出了自动选取聚类中心与截断距离的改进CFSFDP(ICFSFDP)算法;基于KPCA-ICFSFDP和多输出高斯过程(MOGP)算法,按分区建立了多测点变形MOGP回归模型。实例验证结果表明,对于簇内点数量较少的类,相较于不分区的MOGP模型,预测效果得到了一定的提升,同时在整体MOGP模型表现良好的测点,分区后仍然保持较高的预测精度,且与单输出高斯过程模型对比均有所提升。In order to analyze the multi-point data of high arch dams more efficiently,the kernel principal component analysis(KPCA)method for error reconstruction was introduced to identify the outliers in the multi-point sequence.To solve the problem of poor performance of the clustering by fast search and find of density peaks(CFSFDP)algorithm in high dimension,the KPCA method was used to reduce the dimension,and an improved CFSFDP(ICFSFDP)algorithm was proposed for automatic selection of cluster center and truncation distance.Based on KPCA-ICFSFDP method and the multiple output Gaussian process(MOGP)method,a multi-point deformation MOGP model was built according to the partition.The results show that for the small number of partitions within the cluster points,compared with the MOGP model without partition,the prediction effect is improved.Meanwhile,for the measurement points with good performance of the overall MOGP model,the prediction accuracy remains high after partitioning,which is improved compared with the single-output Gaussian process model.

关 键 词：高拱坝 多测点变形 KPCA CFSFDP MOGP 

分 类 号：TV698.1[水利工程—水利水电工程]