A missing data processing method for dam deformation monitoring data using spatiotemporal clustering and support vector machine model

被引：0

作者：

Zhu, Yan-tao ^{[1
,2
]}

Gu, Chong-shi ^{[1
,2
]}

Diaconeasa, Mihai A. ^{[3
]}

机构：

[1] The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing,210098, China

[2] College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing,210098, China

[3] School of Engineering, North Carolina State University, Raleigh,NC,27695, United States

来源：

Water Science and Engineering | 2024年 / 17卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Data assimilation - Data handling - Instrument errors - Spatio-temporal data;

D O I：

10.1016/j.wse.2024.08.003

中图分类号：

学科分类号：

摘要：

Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam. However, the deformation monitoring data are often incomplete due to environmental changes, monitoring instrument faults, and human operational errors, thereby often hindering the accurate assessment of actual deformation patterns. This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data. It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring. The proposed method was validated in a concrete dam project, with the model error maintaining within 5%, demonstrating its effectiveness in processing missing deformation data. This approach enhances the capability of early-warning systems and contributes to enhanced dam safety management. © 2024 Hohai University

引用

页码：417 / 424

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