IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v105y2021i2d10.1007_s11069-020-04353-6.html
   My bibliography  Save this article

An optimal sample selection-based logistic regression model of slope physical resistance against rainfall-induced landslide

Author

Listed:
  • Deliang Sun

    (Chongqing Normal University
    Ministry of Education)

  • Haijia Wen

    (Ministry of Education
    National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas
    Chongqing University)

  • Yalan Zhang

    (Chongqing University)

  • Mengmeng Xue

    (Chongqing University)

Abstract

Due to the insufficient ability of the slope to resist deformation and frequent rainfall, geological disasters often occur on the mountain slopes, especially rainfall-induced landslide. However, the method of assessing the ability of mountain slopes to resist rainfall-induced landslide is still not available. Therefore, using a typical example, this study mainly focuses on developing a mapping model for disaster resistance of rainfall-induced landslide in mountain slopes taking data mining of historical slope damage records and conditioning factors into account. To be specific, specifically, 18 assessment factors, elevation, slope gradient, slope aspect, slope position, plane curvature, section curvature, comprehensive curvature, terrain roughness, CRDS, terrain wetness index, sediment transport index, stream power index, micro-landform, lithology, average annual rainfall, NDVI, distance from faults, and distance from streams, are selected as the evaluation factors for the mountain slope disaster resistance. Afterward, a geospatial database is built to assess the disaster resistance of mountain slopes by combining 477 historical rainfall-induced landslides in the study area. Then, a sample dataset is selected with the negative and positive sample ratio of 1:1. An optimal training sample is selected by tenfold cross-validation, while the testing sample is randomly selected from the sample dataset comprising 30% of the sample dataset. A logistic regression (LR) model is subsequently obtained by the optimal training sample, which contributes to the simulation analysis of the disaster resistance of mountain slopes throughout the study area. Finally, the simulation results are classified into five disaster resistance classes: low, relatively low, medium, relatively high, and high. It should be noted that the area of low and relatively low resistance zones accounts for only 25.06% of the total area, but 83.07% of the historical rainfall-induced landslides are located in this region. While the area of relatively high and high resistance zones accounts for 59.26% of the total area, only 7.77% of the historical rainfall-induced landslides are located there. In addition, an examination based on the receiver operating characteristic (ROC) curve shows that the area under curve values of the testing sample, training sample, sample dataset, and geospatial cells are 0.8552, 0.8924, 0.8605, and 0.8527, respectively. It is found that over the last 1.5 years, the location of 10 rainfall-induced landslides was mainly located in the relatively low and low resistance zones. Thus, it can be concluded that the LR assessment model for disaster resistance of mountain slopes, which is based on a data mining analysis of historical data has high stability and reliability in the assessment of mountain slope disaster resistance against rainfall-induced landslides.

Suggested Citation

  • Deliang Sun & Haijia Wen & Yalan Zhang & Mengmeng Xue, 2021. "An optimal sample selection-based logistic regression model of slope physical resistance against rainfall-induced landslide," 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. 105(2), pages 1255-1279, January.
    • Handle: RePEc:spr:nathaz:v:105:y:2021:i:2:d:10.1007_s11069-020-04353-6
    DOI: 10.1007/s11069-020-04353-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-020-04353-6
    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/s11069-020-04353-6?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. James Gardner & Julie Dekens, 2007. "Mountain hazards and the resilience of social–ecological systems: lessons learned in India and Canada," 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. 41(2), pages 317-336, May.
    2. Chong Xu & Xiwei Xu & Fuchu Dai & Zhide Wu & Honglin He & Feng Shi & Xiyan Wu & Suning Xu, 2013. "Application of an incomplete landslide inventory, logistic regression model and its validation for landslide susceptibility mapping related to the May 12, 2008 Wenchuan earthquake of China," 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. 68(2), pages 883-900, September.
    3. Bilal M. Ayyub, 2014. "Systems Resilience for Multihazard Environments: Definition, Metrics, and Valuation for Decision Making," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 340-355, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jiangping Gao & Xiangyang Shi & Linghui Li & Ziqiang Zhou & Junfeng Wang, 2022. "Assessment of Landslide Susceptibility Using Different Machine Learning Methods in Longnan City, China," Sustainability, MDPI, vol. 14(24), pages 1-26, December.
    2. Thong Xuan Tran & Sihong Liu & Hang Ha & Quynh Duy Bui & Long Quoc Nguyen & Dinh Quoc Nguyen & Cong-Ty Trinh & Chinh Luu, 2024. "A Spatial Landslide Risk Assessment Based on Hazard, Vulnerability, Exposure, and Adaptive Capacity," Sustainability, MDPI, vol. 16(21), pages 1-37, November.
    3. Yuting Yang & Gang Mei, 2022. "A Deep Learning-Based Approach for a Numerical Investigation of Soil–Water Vertical Infiltration with Physics-Informed Neural Networks," Mathematics, MDPI, vol. 10(16), pages 1-19, August.

    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. H. Klammler & P. S. C. Rao & K. Hatfield, 2018. "Modeling dynamic resilience in coupled technological-social systems subjected to stochastic disturbance regimes," Environment Systems and Decisions, Springer, vol. 38(1), pages 140-159, March.
    2. Yusuke Toyoda, 2021. "Survey paper: achievements and perspectives of community resilience approaches to societal systems," Asia-Pacific Journal of Regional Science, Springer, vol. 5(3), pages 705-756, October.
    3. Jesse M. Keenan, 2018. "Regional resilience trust funds: an exploratory analysis for leveraging insurance surcharges," Environment Systems and Decisions, Springer, vol. 38(1), pages 118-139, March.
    4. Yifan Yang & S. Thomas Ng & Frank J. Xu & Martin Skitmore & Shenghua Zhou, 2019. "Towards Resilient Civil Infrastructure Asset Management: An Information Elicitation and Analytical Framework," Sustainability, MDPI, vol. 11(16), pages 1-24, August.
    5. MacKenzie, Cameron A. & Hu, Chao, 2019. "Decision making under uncertainty for design of resilient engineered systems," Reliability Engineering and System Safety, Elsevier, vol. 192(C).
    6. Corinne Curt & Jean‐Marc Tacnet, 2018. "Resilience of Critical Infrastructures: Review and Analysis of Current Approaches," Risk Analysis, John Wiley & Sons, vol. 38(11), pages 2441-2458, November.
    7. Liu, Xing & Ferrario, Elisa & Zio, Enrico, 2019. "Identifying resilient-important elements in interdependent critical infrastructures by sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 423-434.
    8. Mottahedi, Adel & Sereshki, Farhang & Ataei, Mohammad & Qarahasanlou, Ali Nouri & Barabadi, Abbas, 2021. "Resilience estimation of critical infrastructure systems: Application of expert judgment," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    9. Kunal Gupta & Neelima Satyam, 2024. "Optimizing seismic hazard inputs for co-seismic landslide susceptibility mapping: a probabilistic analysis," 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(9), pages 8459-8481, July.
    10. Dui, Hongyan & Liu, Meng & Song, Jiaying & Wu, Shaomin, 2023. "Importance measure-based resilience management: Review, methodology and perspectives on maintenance," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    11. Liu, Xing & Fang, Yi-Ping & Zio, Enrico, 2021. "A Hierarchical Resilience Enhancement Framework for Interdependent Critical Infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    12. Argyroudis, Sotirios A. & Mitoulis, Stergios Aristoteles, 2021. "Vulnerability of bridges to individual and multiple hazards- floods and earthquakes," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    13. Wei Xie & Adam Rose & Shantong Li & Jianwu He & Ning Li & Tariq Ali, 2018. "Dynamic Economic Resilience and Economic Recovery from Disasters: A Quantitative Assessment," Risk Analysis, John Wiley & Sons, vol. 38(6), pages 1306-1318, June.
    14. Nader Naderpajouh & David J. Yu & Daniel P. Aldrich & Igor Linkov & Juri Matinheikki, 2018. "Engineering meets institutions: an interdisciplinary approach to the management of resilience," Environment Systems and Decisions, Springer, vol. 38(3), pages 306-317, September.
    15. Jingjing Kong & Slobodan P. Simonovic, 2019. "Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System," Risk Analysis, John Wiley & Sons, vol. 39(8), pages 1843-1863, August.
    16. Dmitry Borisoglebsky & Liz Varga, 2019. "A Resilience Toolbox and Research Design for Black Sky Hazards to Power Grids," Complexity, Hindawi, vol. 2019, pages 1-15, June.
    17. Amanda Melendez & David Caballero-Russi & Mariantonieta Gutierrez Soto & Luis Felipe Giraldo, 2022. "Computational models of community resilience," 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. 111(2), pages 1121-1152, March.
    18. Das, Laya & Munikoti, Sai & Natarajan, Balasubramaniam & Srinivasan, Babji, 2020. "Measuring smart grid resilience: Methods, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    19. Cassottana, Beatrice & Shen, Lijuan & Tang, Loon Ching, 2019. "Modeling the recovery process: A key dimension of resilience," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    20. Bruno Martinho & João Reis, 2022. "United Nations (UN) Disaster Risk Reduction Framework: Case Study of the Portuguese Army on UN Challenges in the Context of Sustainable Risk Mitigation," Sustainability, MDPI, vol. 14(3), pages 1-17, February.

    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:105:y:2021:i:2:d:10.1007_s11069-020-04353-6. 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.