Volume 17 Issue 4
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Yan-tao Zhu, Chong-shi Gu, Mihai A. Diaconeasa. 2024: A missing data processing method for dam deformation monitoring data using spatiotemporal clustering and support vector machine model. Water Science and Engineering, 17(4): 417-424. doi: 10.1016/j.wse.2024.08.003
Citation: Yan-tao Zhu, Chong-shi Gu, Mihai A. Diaconeasa. 2024: A missing data processing method for dam deformation monitoring data using spatiotemporal clustering and support vector machine model. Water Science and Engineering, 17(4): 417-424. doi: 10.1016/j.wse.2024.08.003
shu

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

doi: 10.1016/j.wse.2024.08.003

  • a. The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing 210098, China;

  • b. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China;

  • c. School of Engineering, North Carolina State University, Raleigh, NC 27695, USA

Funds:

This work was supported by the National Key R&D Program of China (Grant No.2022YFC3005401),the Fundamental Research Funds for the Central Universities (Grant No.B230201013),the National Natural Science Foundation of China (Grants No.52309152,U2243223,and U23B20150),the Natural Science Foundation of Jiangsu Province (Grant No.BK20220978),and the Open Fund of National Dam Safety Research Center (Grant No.CX2023B03).

  • Received Date: 2023-12-20
  • Accepted Date: 2024-08-26
    Abstract
  • 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.

     

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