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The Coupled Secondary Mode Decomposition and Optimized LightGBM Model for Dam Deformation Prediction
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Hao KONG1, Yong DING1, *, Denghua LI2, 3
Journal of Geodesy and Geodynamics | 2025, 45(11) : 1171 - 1179
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Journal of Geodesy and Geodynamics | 2025, 45(11): 1171-1179
The Coupled Secondary Mode Decomposition and Optimized LightGBM Model for Dam Deformation Prediction
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Hao KONG1, Yong DING1, *, Denghua LI2, 3
Affiliations
  • 1 School of Safety Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China
  • 2 Nanjing Hydraulic Research Institute, 34 Hujuguan, Nanjing 210029, China
  • 3 Key Laboratory of Reservoir and Dam Safety, MWR, 34 Hujuguan, Nanjing 210029, China

    • KONG Hao, postgraduate, majors in structural health monitoring, E-mail:

About Author:

KONG Hao, postgraduate, majors in structural health monitoring, E-mail:

Published: 2025-11-15 doi: 10.14075/j.jgg.2024.12.548
Outline
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A dam deformation prediction model is proposed that integrates secondary mode decomposition with butterfly optimization algorithm(BOA)-optimized light gradient boosting machine(LightGBM). First, the training set data is decomposed using complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN), and the composite entropy of decomposed subsequences is calculated. Second, the K-means clustering algorithm is used to categorize the decomposed subsequences into high and low frequency components, and variational mode decomposition(VMD) is applied to high frequency signals. Finally, the BOA-optimized LightGBM model is employed for prediction. The case studies demonstrate that this method effectively processes deformation data and enhances data stationarity, achieving significant accuracy improvements compared with conventional approaches. The nMAPE, MSE, and MAE indexes improve 16.2%-22.5%, 16.8%-28.1%, and 16.2%-22.5%, respectively.

secondary mode decomposition  /  high and low frequency signal classification  /  butterfly optimization algorithm  /  light gradient boosting machine  /  deformation prediction
孔颢, 丁勇, 李登华. 耦合二次模态分解和优化LightGBM的大坝变形预测模型. 大地测量与地球动力学, 2025 , 45 (11) : 1171 -1179 . DOI: 10.14075/j.jgg.2024.12.548
Hao KONG, Yong DING, Denghua LI. The Coupled Secondary Mode Decomposition and Optimized LightGBM Model for Dam Deformation Prediction[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (11) : 1171 -1179 . DOI: 10.14075/j.jgg.2024.12.548
Funding
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  • National Key Research and Development Program of China(2024YFC3210703)
  • National Natural Science Foundation of China(U2240221)
  • National Natural Science Foundation of China(51979174)
  • Special Fund for Basic Scientific Research of Central Public Research Institutes(Y724011)
Appendix
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Year 2025 volume 45 Issue 11
29
11
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Article Info
doi: 10.14075/j.jgg.2024.12.548
  • Receive Date：2024-12-02
  • Online Date：2026-10-29
  • Published：2025-11-15
Article Data
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History
  • Received：2024-12-02
Funding
National Key Research and Development Program of China(2024YFC3210703)
National Natural Science Foundation of China(U2240221)
National Natural Science Foundation of China(51979174)
Special Fund for Basic Scientific Research of Central Public Research Institutes(Y724011)
Affiliations
    1 School of Safety Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei Street, Nanjing 210094, China
    2 Nanjing Hydraulic Research Institute, 34 Hujuguan, Nanjing 210029, China
    3 Key Laboratory of Reservoir and Dam Safety, MWR, 34 Hujuguan, Nanjing 210029, China
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https://castjournals.cast.org.cn/joweb/ddcl/EN/10.14075/j.jgg.2024.12.548
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