(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.山东省华诚工程咨询监理有限公司,山东 高密261500)
(1.College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;2.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China;3.Shandong Huacheng Engineering Consulting and Supervision Co., Ltd., Gaomi 261500, China)
To solve the problem that the internal correlation between multiple measuring points cannot be considered by traditional single measuring point monitoring models, which is difficult to reflect the regional characteristics of high arch dam deformation in space. A high arch dam deformation prediction model based on clustering partition and a multi-output least square support vector regression machine (MO-LSSVR) algorithm is proposed. Based on the composite similarity index between the measuring points, the clustering partition of spatial correlation measuring points is realized by hierarchical agglomerative clustering (HAC) algorithm. The MO-LSSVR algorithm integrating the correlation characteristics of measuring points is then used to model the points in the partition. The engineering example results show that the clustering partition results are consistent with the spatial distribution characteristics of dam deformation. The MO-LSSVR model based on the reasonable partition results has high accuracy and robustness, which provides a new method to accurately predict the dam deformation and monitoring the overall safety state of the dam from the multi-measuring points correlation dimension.
刘伟琪,陈波,葛盼猛,等.基于聚类分区和MO-LSSVR的高拱坝变形预测模型[J].水利水电科技进展,2023,43(2):102-108.(LIU Weiqi, CHEN Bo, GE Panmeng, et al. Deformation prediction model of a high arch dam based on clustering and MO-LSSVR[J]. Advances in Science and Technology of Water Resources,2023,43(2):102-108.(in Chinese))复制