Volume 43 Issue 5
Sep.  2024
Turn off MathJax
Article Contents
GENG Fang, BAI Suna, QI Wenyan, YU Jinshan, MAO Hua, ZHANG Mei, XI Xueping, GAO Xuefei, LUO Fugui. Investigations into ground subsidence in Tianjin coastal area based on random forest[J]. Bulletin of Geological Science and Technology, 2024, 43(5): 197-205. doi: 10.19509/j.cnki.dzkq.tb20240119
Citation: GENG Fang, BAI Suna, QI Wenyan, YU Jinshan, MAO Hua, ZHANG Mei, XI Xueping, GAO Xuefei, LUO Fugui. Investigations into ground subsidence in Tianjin coastal area based on random forest[J]. Bulletin of Geological Science and Technology, 2024, 43(5): 197-205. doi: 10.19509/j.cnki.dzkq.tb20240119

Investigations into ground subsidence in Tianjin coastal area based on random forest

doi: 10.19509/j.cnki.dzkq.tb20240119
More Information
  • Objective

    The spatial distribution of ground subsidence in the coastal area of Tianjin was predicted using a random forest machine learning model, in which the performance and significance of the variables were evaluated.

    Methods

    The random forest model was trained and validated in this study using datasets of ground subsidence in 2020, aquifer lithology, water level differences in aquifers in 2020, and hydrogeological parameters.

    Results

    The results demonstrate the effectiveness of the random forest model for fitting and predicting ground subsidence (R2=0.98, RMSE=0.52 mm). Moreover, it is found that water level difference emerges as the most influential factor affecting ground subsidence, followed by lithology and hydrogeological parameters.

    Conclusion

    The present study introduces several novel contributions: ① utilization of spatial distribution data for training ground subsidence models; ② identification of significant controlling factors based on physical mechanisms; ③ assessment of the relative importance of these controlling factors. Additionally, this paper highlights the limitations and future directions in ground subsidence research, offering valuable insights for the rapid and accurate prediction of ground subsidence using the random forest model.

     

  • The authors declare that no competing interests exist.
  • loading
  • [1]
    HERRERA-GARCÍA G, EZQUERRO P, TOMÁS R, et al. Mapping the global threat of land subsidence[J]. Science, 2021, 371: 34-36. doi: 10.1126/science.abb8549
    [2]
    李文鹏, 王龙凤, 郭海朋, 等. 中国地面沉降防治成效与对策建议[J]. 中国水利, 2021(7): 32-35.

    LI W P, WANG L F, GUO H P, et al. Effectiveness and countermeasures of land subsidence control in China[J]. China Water Resources, 2021(7): 32-35. (in Chinese with English abstract)
    [3]
    秦欢欢. 北京平原地面沉降数值模拟情景分析[J]. 地质科技情报, 2019, 38(1): 221-227.

    QIN H H. Numerical simulation and scenario analysis of land subsidence in Beijing Plain[J]. Geological Science and Technology Information, 2019, 38(1): 221-227. (in Chinese with English abstract)
    [4]
    胡喜梅, 马传明, 邓波, 等. 江苏省沿海地区地面沉降风险评价[J]. 地质科技情报, 2017, 36(2): 222-228.

    HU X M, MA C M, DENG B, et al. Risk evaluation of land subsidence in coastal areas of Jiangsu Province[J]. Geological Science and Technology Information, 2017, 36(2): 222-228. (in Chinese with English abstract)
    [5]
    薛禹群, 张云, 叶淑君, 等. 我国地面沉降若干问题研究[J]. 高校地质学报, 2006, 12(2): 153-160.

    XUE Y Q, ZHANG Y, YE S J, et al. Research on the problems of land subsidence in China[J]. Geological Journal of China Universities, 2006, 12(2): 153-160. (in Chinese with English abstract)
    [6]
    罗跃, 叶淑君, 吴吉春. 三维区域地面沉降数值模拟[J]. 岩土力学, 2018, 39(3): 1063-1070.

    LUO Y, YE S J, WU J C. Numerical model for simulating 3D regional land subsidence[J]. Rock and Soil Mechanics, 2018, 39(3): 1063-1070. (in Chinese with English abstract)
    [7]
    王礼春. 天津市深层地下水资源及其地面沉降数值模拟研究[D]. 北京: 中国地质大学(北京), 2010.

    WANG L C. The study on deep groundwater resources and subsidence caused by withdrawal with method of numerical simulation in Tianjin district[D]. Beijing: China University of Geosciences(Beijing), 2010. (in Chinese with English abstract)
    [8]
    SHI X Q, WU J C, YE S J, et al. Regional land subsidence simulation in Su-Xi-Chang area and Shanghai City, China[J]. Engineering Geology, 2008, 100(1/2): 27-42.
    [9]
    MOHAMMADY M, POURGHASEMI H R, AMIRI M. Land subsidence susceptibility assessment using random forest machine learning algorithm[J]. Environmental Earth Sciences, 2019, 78(16): 503. doi: 10.1007/s12665-019-8518-3
    [10]
    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 Q H, ZHANG Y H, DENG M, 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)
    [11]
    YU H R, GONG H L, CHEN B B, et al. Analysis of the influence of groundwater on land subsidence in Beijing based on the geographical weighted regression(GWR) model[J]. Science of the Total Environment, 2020, 738: 139405. doi: 10.1016/j.scitotenv.2020.139405
    [12]
    杨霄. 菏泽市地面沉降因子识别体系与预测评估模型研究[D]. 济南: 山东大学, 2021.

    YANG X. Research on the identification system and prediction evaluation model of land subsidence factors in Heze City[D]. Jinan: Shandong University, 2021. (in Chinese with English abstract)
    [13]
    BIAU G, SCORNET E. A random forest guided tour[J]. TEST, 2016, 25(2): 197-227. doi: 10.1007/s11749-016-0481-7
    [14]
    司新毅, 谢新民, 李盛. 基于PS-InSAR和随机森林的天津区域地表形变监测[J]. 大地测量与地球动力学, 2023, 43(7): 692-695.

    SI X Y, XIE X M, LI S. Surface deformation monitoring of Tianjin area based on PS-InSAR and random forest[J]. Journal of Geodesy and Geodynamics, 2023, 43(7): 692-695. (in Chinese with English abstract)
    [15]
    焦珣, 吴建中, 王寒梅, 等. 软土地区地铁道床沉降特征及其诱发因素分析[J]. 世界地质, 2016, 35(1): 276-282.

    JIAO X, WU J Z, WANG H M, et al. Characteristics of track-bed settlement and its inducing factors for subway in soft soil districts[J]. Global Geology, 2016, 35(1): 276-282. (in Chinese with English abstract)
    [16]
    张倩, 马悦, 周洪月, 等. 基于InSAR技术的天津局部地表沉降特征分析[J]. 测绘通报, 2024(2): 74-79.

    ZHANG Q, MA Y, ZHOU H Y, et al. Analysis of local surface subsidence characteristics in Tianjin based on InSAR technology[J]. Bulletin of Surveying and Mapping, 2024(2): 74-79. (in Chinese with English abstract)
    [17]
    李佳琦, 徐佳, 刘杰, 等. 天津地面沉降严重区分布特征及变化规律[J]. 中国地质灾害与防治学报, 2023, 34(2): 53-60.

    LI J Q, XU J, LIU J, et al. Distribution characteristics and evolution trend of severe land subsidence areas in Tianjin City[J]. The Chinese Journal of Geological Hazard and Control, 2023, 34(2): 53-60. (in Chinese with English abstract)
    [18]
    张庆云, 李雪川, 王宁. 基于SBAS技术的天津市地面形变特征分析[J]. 科学技术与工程, 2022, 22(30): 13202-13210.

    ZHANG Q Y, LI X C, WANG N. Analysis of ground deformation characteristics in Tianjin based on SBAS technology[J]. Science Technology and Engineering, 2022, 22(30): 13202-13210. (in Chinese with English abstract)
    [19]
    吕潇文. 天津临港工业区地面沉降特征研究[D]. 北京: 中国地质大学(北京), 2014. LÜ X W.

    Study on the characteristics of land subsidence disaster in Tianjin harbor industrial park[D]. Beijing: China University of Geosciences(Beijing), 2014. (in Chinese with English abstract)
    [20]
    于海若, 宫辉力, 陈蓓蓓, 等. 天津市南部平原地面沉降区新兴风险评估[J]. 自然资源遥感, 2023, 35(2): 182-192.

    YU H R, GONG H L, CHEN B B, et al. Emerging risk assessment of areas subject to land subsidence in the southern plain of Tianjin, China[J]. Remote Sensing for Natural Resources, 2023, 35(2): 182-192. (in Chinese with English abstract)
    [21]
    SHI L Y, GONG H L, CHEN B B, et al. Land subsidence prediction induced by multiple factors using machine learning method[J]. Remote Sensing, 2020, 12(24): 4044.
    [22]
    PEDREGOSA F, VAROQUAUX G, GRAMFORT A, et al. Scikit-learn: Machine learning in Python[J]. The Journal of Machine Learning Research, 2011, 12: 2825-2830.
    [23]
    SEKKERAVANI M A, BAZRAFSHAN O, POURGHASEMI H R, et al. Spatial modeling of land subsidence using machine learning models and statistical methods[J]. Environmental Science and Pollution Research International, 2022, 29(19): 28866-28883.
    [24]
    段永侯, 王家兵, 王亚斌, 等. 天津市地下水资源与可持续利用[J]. 水文地质工程地质, 2004, 31(3): 29-39.

    DUAN Y H, WANG J B, WANG Y B, et al. Groundwater resources and its sustainable development in Tianjin[J]. Hydrogeology and Engineering Geology, 2004, 31(3): 29-39. (in Chinese with English abstract)
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article Views(110) PDF Downloads(24) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return