IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v120y2024i14d10.1007_s11069-024-06737-4.html
   My bibliography  Save this article

Study on slope deformation partition and monitoring point optimization considering spatial correlation

Author

Listed:
  • Yuanzheng Li

    (Chengdu University of Technology
    Chengdu University of Technology)

  • Weixin Zhang

    (Hefei University of Technology)

  • Kaiqiang Zhang

    (Chongqing University
    Chongqing 208 Geo-Environmental Research Institute Co. Ltd)

  • Qingsong Gou

    (Chongqing Institute of Geology and Mineral Resources)

  • Song Tang

    (Chengdu University of Technology
    Chengdu University of Technology)

  • Fulin Guo

    (Chengdu University of Technology
    Chengdu University of Technology)

Abstract

Slope displacement prediction is an effective means to realize slope disaster early warning and prediction. The scientific and reasonable layout of surface displacement monitoring points is the basis of slope disaster prediction. There are some defects in the determination of the number and location of monitoring points in the process of laying, and there needs to be an effective regulation on how to lay monitoring points according to the overall deformation characteristics of the slope. Therefore, combining the displacement data sequence and spatial location information of the surface displacement monitoring points, based on the correlation of the displacement sequence between different monitoring points, quantifying the spatial correlation of the monitoring points, introducing the normalized spatial correlation scale index, using the cluster analysis method considering the spatial correlation of the monitoring points to divide the deformation area of the slope, and removing the redundant monitoring points in each deformation area according to the result of the deformation area division, Finally, the overall optimization of slope monitoring points is realized. Through this method, slope deformation zoning is more scientific and reasonable, avoiding the influence of human subjectivity on the site and scientifically solving the data overlap phenomenon caused by the unreasonable arrangement of surface displacement monitoring points, thus improving the efficiency and accuracy of slope monitoring. In order to verify the effectiveness of the method, a sand–mudstone interbedded Counter-Tilt excavation slope in southwest China was used as the research object. And 24 monitoring points deployed on this slope were monitored for surface displacement for 13 months. The spatial location of the monitoring points was discussed. The results show that the proposed method of slope deformation zoning and the optimized placement of monitoring points are feasible.

Suggested Citation

  • Yuanzheng Li & Weixin Zhang & Kaiqiang Zhang & Qingsong Gou & Song Tang & Fulin Guo, 2024. "Study on slope deformation partition and monitoring point optimization considering spatial correlation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(14), pages 13109-13136, November.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:14:d:10.1007_s11069-024-06737-4
    DOI: 10.1007/s11069-024-06737-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-024-06737-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-024-06737-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Struyf, Anja & Hubert, Mia & Rousseeuw, Peter J., 1997. "Integrating robust clustering techniques in S-PLUS," Computational Statistics & Data Analysis, Elsevier, vol. 26(1), pages 17-37, November.
    2. K. Hastaoglu & D. Sanli, 2011. "Monitoring Koyulhisar landslide using rapid static GPS: a strategy to remove biases from vertical velocities," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 58(3), pages 1275-1294, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dario Krpan & Jonathan E. Booth & Andreea Damien, 2023. "The positive–negative–competence (PNC) model of psychological responses to representations of robots," Nature Human Behaviour, Nature, vol. 7(11), pages 1933-1954, November.
    2. Aloyce R Kaliba & Kizito Mazvimavi & Theresia L Gregory & Frida M Mgonja & Mary Mgonja, 2018. "Factors affecting adoption of improved sorghum varieties in Tanzania under information and capital constraints," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 6(1), pages 1-21, December.
    3. Song, Seongjoo & Nicolae, Dan L. & Song, Jongwoo, 2010. "Estimating the mixing proportion in a semiparametric mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2276-2283, October.
    4. Slaets, Leen & Claeskens, Gerda & Hubert, Mia, 2012. "Phase and amplitude-based clustering for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2360-2374.
    5. Birgin, E. G. & Martinez, J. M. & Ronconi, D. P., 2003. "Minimization subproblems and heuristics for an applied clustering problem," European Journal of Operational Research, Elsevier, vol. 146(1), pages 19-34, April.
    6. Ghosh Samiran & Townsend Jeffrey P., 2015. "H-CLAP: hierarchical clustering within a linear array with an application in genetics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(2), pages 125-141, April.
    7. Pison, Greet & Struyf, Anja & Rousseeuw, Peter J., 1999. "Displaying a clustering with CLUSPLOT," Computational Statistics & Data Analysis, Elsevier, vol. 30(4), pages 381-392, June.
    8. Ruya Xiao & Xiufeng He, 2013. "Real-time landslide monitoring of Pubugou hydropower resettlement zone using continuous GPS," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 69(3), pages 1647-1660, December.
    9. Ahmed Albatineh & Magdalena Niewiadomska-Bugaj, 2011. "MCS: A Method for Finding the Number of Clusters," Journal of Classification, Springer;The Classification Society, vol. 28(2), pages 184-209, July.
    10. Karina Acosta & Yuri Reina-Aranza, 2023. "Categorías municipales en Colombia: Avanzando hacia un modelo de descentralización asimétrica," Documentos de trabajo sobre Economía Regional y Urbana 321, Banco de la Republica de Colombia.
    11. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    12. Abellanas, Manuel & Claverol, Merce & Hurtado, Ferran, 2007. "Point set stratification and Delaunay depth," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2513-2530, February.
    13. Guoquan Wang & Yan Bao & Yanet Cuddus & Xueyi Jia & John Serna & Qi Jing, 2015. "A methodology to derive precise landslide displacement time series from continuous GPS observations in tectonically active and cold regions: a case study in Alaska," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(3), pages 1939-1961, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:120:y:2024:i:14:d:10.1007_s11069-024-06737-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.