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JIANG Jinjin,LIU Jia,JIANG Shan,et al. Research on Multivariate Regression LSTM Model for Predicting Soft Soil Ground Settlement in Zhuhai City[J]. The Chinese Journal of Geological Hazard and Control,2025,36(0): 1-8. DOI: 10.16031/j.cnki.issn.1003-8035.202410009
Citation: JIANG Jinjin,LIU Jia,JIANG Shan,et al. Research on Multivariate Regression LSTM Model for Predicting Soft Soil Ground Settlement in Zhuhai City[J]. The Chinese Journal of Geological Hazard and Control,2025,36(0): 1-8. DOI: 10.16031/j.cnki.issn.1003-8035.202410009

Research on Multivariate Regression LSTM Model for Predicting Soft Soil Ground Settlement in Zhuhai City

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  • Received Date: September 25, 2024
  • Revised Date: February 13, 2025
  • Accepted Date: May 13, 2025
  • Available Online: May 29, 2025
  • In response to the urban safety concerns caused by ground settlement in soft soil zones of Zhuhai City, this research focuses on the development and optimization of a multivariate prediction model. Recognizing the limitations of traditional prediction methods in modeling nonlinear settlement behavior, a novel multivariate regression LSTM prediction model is proposed, based on the characteristics of soft soils in the region. The model fully integrates InSAR monitoring data with various nonlinear influencing factors. Ten key influencing factors, including groundwater extraction intensity, soft soil layer thickness, and compression modulus, were systematically selected. Leveraging the LSTM’s gated structure, the model successfully eliminates the reliance on the time-sensitive physical parameters and the completeness of monitoring data typical of conventional methods. The results demonstrates strong predictive performance: over 88% of errors fall within ± 5mm, and the R2 coefficient of the test set reaches as high as 0.91, indicating the model’s high accuracy and reliability. Further enhancement through intelligent optimization algorithms significantly improved hyperparameter tuning and feature selection, pushing the R2 above 0.98. However, the model’s performance in geologically complex or highly heterogeneous regions still depends on the integration of diverse monitoring technologies to ensure data validity and model precision. Practical application suggests that the model can be effectively used in urban planning, disaster prevention and mitigation, providing reliable land subsidence data for government agencies and experts. Its adaptive learning mechanism holds significant potential for broader application in other similar soft soil regions across the Pearl River Delta.

  • [1]
    刘青豪,张永红,邓敏,等. 大范围地表沉降时序深度学习预测法[J]. 测绘学报,2021,50(3):396 − 404. [LIU Qinghao,ZHANG Yonghong,DENG Min,et al. Time series prediction method of large-scale surface subsidence based on deep learning[J]. Acta Geodaetica et Cartographica Sinica,2021,50(3):396 − 404. (in Chinese with English abstract)]

    LIU Qinghao, ZHANG Yonghong, DENG Min, et al. Time series prediction method of large-scale surface subsidence based on deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(3): 396 − 404. (in Chinese with English abstract)
    [2]
    朱宝,罗孝文,吴自银. 基于ARIMA与LSTM的海岸带地面沉降预测方法——以杭州湾地区为例[J]. 海洋学研究,2022,40(2):53 − 61. [ZHU Bao,LUO Xiaowen,WU Ziyin. ARIMA- and LSTM-based forecasting method of land subsidence in coastal zone:A case study from the Hangzhou Bay and its adjacent area[J]. Journal of Marine Sciences,2022,40(2):53 − 61. (in Chinese with English abstract)]

    ZHU Bao, LUO Xiaowen, WU Ziyin. ARIMA- and LSTM-based forecasting method of land subsidence in coastal zone: A case study from the Hangzhou Bay and its adjacent area[J]. Journal of Marine Sciences, 2022, 40(2): 53 − 61. (in Chinese with English abstract)
    [3]
    江金进,刘佳,吴舒天,等. 珠海市软土分布特征及软土沉降风险评价[J]. 地质灾害与环境保护,2020,31(2):68 − 74. [JIANG Jinjin,LIU Jia,WU Shutian,et al. Distribution characteristics of soft soil and risk assessment of soft soil subsidence in Zhuhai[J]. Journal of Geological Hazards and Environment Preservation,2020,31(2):68 − 74. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1006-4362.2020.02.012

    JIANG Jinjin, LIU Jia, WU Shutian, et al. Distribution characteristics of soft soil and risk assessment of soft soil subsidence in Zhuhai[J]. Journal of Geological Hazards and Environment Preservation, 2020, 31(2): 68 − 74. (in Chinese with English abstract) DOI: 10.3969/j.issn.1006-4362.2020.02.012
    [4]
    江金进,刘佳,等. 软土地面沉降调查评价和机理研究成果报告[R]. 广东省地质局第一地质大队(广东省珠海地质灾害应急抢险技术中心),2024. [JIANG Jinjin,LIU Jia,et al. Report on investigation,evaluation and mechanism research of soft soil ground settlement [R]. First Geological Brigade of Guangdong Geological Bureau (Zhuhai Geological Disaster Emergency Rescue Technology Center,Guangdong Province),2024. (in Chinese)]

    JIANG Jinjin, LIU Jia, et al. Report on investigation, evaluation and mechanism research of soft soil ground settlement [R]. First Geological Brigade of Guangdong Geological Bureau (Zhuhai Geological Disaster Emergency Rescue Technology Center, Guangdong Province), 2024. (in Chinese)
    [5]
    HOCHREITER S,SCHMIDHUBER J. Long short-term memory[J]. Neural Computation,1997,9(8):1735 − 1780. DOI: 10.1162/neco.1997.9.8.1735
    [6]
    高眯眯. 基于深度学习的变形监测数据处理与分析——以唐山南湖地区为例[D]. 唐山:华北理工大学,2022. [GAO Mimi. Processing and analysis of deformation monitoring data based on deep learning[D]. Tangshan:North China University of Science and Technology,2022. (in Chinese with English abstract)]

    GAO Mimi. Processing and analysis of deformation monitoring data based on deep learning[D]. Tangshan: North China University of Science and Technology, 2022. (in Chinese with English abstract)
    [7]
    李广信,张丙印,于玉贞. 土力学[M]. 3版. 北京:清华大学出版社,2022. [LI Guangxin,ZHANG Bingyin,YU Yuzhen. Soil mechanics[M]. 3rd ed. Beijing:Tsinghua University Press,2022. (in Chinese)]

    LI Guangxin, ZHANG Bingyin, YU Yuzhen. Soil mechanics[M]. 3rd ed. Beijing: Tsinghua University Press, 2022. (in Chinese)
    [8]
    王双,严学新,揭江,等. 珠三角平原区软土分布与地面沉降相关性分析[J]. 上海国土资源,2019,40(2):75 − 79. [WANG Shuang,YAN Xuexin,JIE Jiang,et al. Correlation analysis between soft soil distribution and land subsidence in the Pearl River Delta Plain[J]. Shanghai Land & Resources,2019,40(2):75 − 79. (in Chinese with English abstract)] DOI: 10.3969/j.issn.2095-1329.2019.02.015

    WANG Shuang, YAN Xuexin, JIE Jiang, et al. Correlation analysis between soft soil distribution and land subsidence in the Pearl River Delta Plain[J]. Shanghai Land & Resources, 2019, 40(2): 75 − 79. (in Chinese with English abstract) DOI: 10.3969/j.issn.2095-1329.2019.02.015
    [9]
    王双,严学新,揭江,等. 珠江三角洲平原区地面沉降影响因素分析[J]. 中国地质灾害与防治学报,2019,30(5):98 − 104. [WANG Shuang,YAN Xuexin,JIE Jiang,et al. Analysis on factors affecting ground settlement in plain area of Pearl River Delta[J]. The Chinese Journal of Geological Hazard and Control,2019,30(5):98 − 104. (in Chinese with English abstract)]

    WANG Shuang, YAN Xuexin, JIE Jiang, et al. Analysis on factors affecting ground settlement in plain area of Pearl River Delta[J]. The Chinese Journal of Geological Hazard and Control, 2019, 30(5): 98 − 104. (in Chinese with English abstract)
    [10]
    梁景才. 珠江三角洲平原区地面沉降成因机理分析[J]. 测绘与空间地理信息,2022,45(5):162 − 165. [LIANG Jingcai. Analysis on the causes of land subsidence in the Pearl River Delta Plain[J]. Geomatics & Spatial Information Technology,2022,45(5):162 − 165. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1672-5867.2022.05.045

    LIANG Jingcai. Analysis on the causes of land subsidence in the Pearl River Delta Plain[J]. Geomatics & Spatial Information Technology, 2022, 45(5): 162 − 165. (in Chinese with English abstract) DOI: 10.3969/j.issn.1672-5867.2022.05.045
    [11]
    李伟,刘军. 常州典型区地面沉降演化特征与成因机理分析[J]. 城市勘测,2024(2):195 − 198. [LI Wei,LIU Jun. Evolution characteristics and genetic mechanism of land subsidence in typical area of Changzhou[J]. Urban Geotechnical Investigation & Surveying,2024(2):195 − 198. (in Chinese with English abstract)] DOI: 10.3969/j.issn.1672-8262.2024.02.048

    LI Wei, LIU Jun. Evolution characteristics and genetic mechanism of land subsidence in typical area of Changzhou[J]. Urban Geotechnical Investigation & Surveying, 2024(2): 195 − 198. (in Chinese with English abstract) DOI: 10.3969/j.issn.1672-8262.2024.02.048
    [12]
    张宏雪. 基于InSAR与机器学习的延安新区沉降监测与预测研究[D]. 兰州:兰州大学,2021. [ZHANG Hongxue. Research on monitoring and prediction of subsidence in Yan’an new area based on InSAR and machine learning[D]. Lanzhou:Lanzhou University,2021. (in Chinese with English abstract)]

    ZHANG Hongxue. Research on monitoring and prediction of subsidence in Yan’an new area based on InSAR and machine learning[D]. Lanzhou: Lanzhou University, 2021. (in Chinese with English abstract)
    [13]
    JI Xinying,TANG Jiali,ZHANG Junpei. Effects of salt stress on the morphology,growth and physiological parameters of juglansmicrocarpa L. seedlings[J]. Plants,2022,11(18):2381. DOI: 10.3390/plants11182381
    [14]
    XUE Jiankai,SHEN Bo. A novel swarm intelligence optimization approach:Sparrow search algorithm[J]. Systems Science & Control Engineering,2020,8(1):22 − 34.
    [15]
    XUE Jiankai,SHEN Bo. Dung beetle optimizer:A new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing,2023,79(7):7305 − 7336. DOI: 10.1007/s11227-022-04959-6
    [16]
    MIRJALILI S. SCA:A Sine Cosine Algorithm for solving optimization problems[J]. Knowledge-Based Systems,2016,96:120 − 133. DOI: 10.1016/j.knosys.2015.12.022
    [17]
    ZHAN Zhihui,ZHANG Jun. Adaptive particle swarm optimization[M]//Ant Colony Optimization and Swarm Intelligence. Berlin,Heidelberg:Springer Berlin Heidelberg,2008:227 − 234.
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