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A Deep Learning Landslide Displacement Prediction Model Combining Time Series Decomposition and Similar Component Reorganization
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Wei QU1, 2, Da LI1, Jiuyuan LI1, Zice BIAN1
Journal of Geodesy and Geodynamics | 2025, 45(3) : 221 - 230
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Journal of Geodesy and Geodynamics | 2025, 45(3): 221-230
A Deep Learning Landslide Displacement Prediction Model Combining Time Series Decomposition and Similar Component Reorganization
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Wei QU1, 2, Da LI1, Jiuyuan LI1, Zice BIAN1
Affiliations
  • 1 School of Geological Engineering and Geomatics, Chang'an University, 126 Yanta Road, Xi'an 710054, China
  • 2 Key Laboratory of Western China's Mineral Resources and Geological Engineering, Ministry of Education, 126 Yanta Road, Xi'an 710054, China
  • QU Wei, PhD, professor, PhD supervisor, majors in geological disaster high-precision monitoring and their genesis mechanism, E-mail:

About Author:

QU Wei, PhD, professor, PhD supervisor, majors in geological disaster high-precision monitoring and their genesis mechanism, E-mail:

Published: 2025-03-15 doi: 10.14075/j.jgg.2024.04.186
Outline
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On the basis of effectively handling the gross errors of landslide monitoring data and fully considering the characteristics of landslide monitoring data, we develop a deep learning landslide displacement prediction model combining time series decomposition and similar component reorganization. First, we deal with the significant gross errors of landslide time series monitoring data using the isolation forest algorithm, and then comprehensively analyze its smoothness, autocorrelation, and normality to determine the optimal length of input feature sequence. Second, the non-stationary landslide monitoring data are decomposed into multiple smooth time series using the ensemble empirical mode decomposition(EEMD) method, which is then classified into three types combining the sample entropy and K-means algorithm, namely high, medium, and low frequency. Finally, comparing the prediction accuracy of different neural networks, the prediction models suitable for three types of time components are constructed respectively, and then the prediction results are superimposed to realize the high-precision prediction of landslide displacement. The testing results of Beidou/GNSS monitoring data of typical landslide body in experimental area show that the combination prediction model proposed in this paper has a better applicability to the landslide monitoring data containing significant gross errors, and can significantly improve the prediction accuracy of landslide displacement compared with single and existing combination models.

landslide displacement prediction  /  ensemble empirical mode decomposition(EEMD)  /  sample entropy  /  deep neural network  /  temporal convolutional network
瞿伟, 李达, 李久元, 边子策. 一种结合时序分解与相似分量重组的深度学习滑坡位移组合预测模型. 大地测量与地球动力学, 2025 , 45 (3) : 221 -230 . DOI: 10.14075/j.jgg.2024.04.186
Wei QU, Da LI, Jiuyuan LI, Zice BIAN. A Deep Learning Landslide Displacement Prediction Model Combining Time Series Decomposition and Similar Component Reorganization[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (3) : 221 -230 . DOI: 10.14075/j.jgg.2024.04.186
  • National Natural Science Foundation of China(42174006)
  • National Natural Science Foundation of China(42090055)
  • Science Fund for Distinguished Young Scholars of Shaanxi Province(2022JC-18)
  • Fundamental Research Funds for the Central Universities(CHD300102263201)
Year 2025 volume 45 Issue 3
26
10
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Article Info
doi: 10.14075/j.jgg.2024.04.186
  • Receive Date:2024-04-18
  • Online Date:2026-10-30
  • Published:2025-03-15
Article Data
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History
  • Received:2024-04-18
Funding
National Natural Science Foundation of China(42174006)
National Natural Science Foundation of China(42090055)
Science Fund for Distinguished Young Scholars of Shaanxi Province(2022JC-18)
Fundamental Research Funds for the Central Universities(CHD300102263201)
Affiliations
    1 School of Geological Engineering and Geomatics, Chang'an University, 126 Yanta Road, Xi'an 710054, China
    2 Key Laboratory of Western China's Mineral Resources and Geological Engineering, Ministry of Education, 126 Yanta Road, Xi'an 710054, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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