IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v14y2025i2p355-d1587137.html
   My bibliography  Save this article

Geomorphological and Geological Characteristics Slope Unit: Advancing Township-Scale Landslide Susceptibility Assessment Strategies

Author

Listed:
  • Gang Chen

    (Institute for Geological Disaster Risk Prevention and Control in Western Region, Nanjiang Hydrogeological and Engineering Geological Team, Chongqing 401120, China)

  • Taorui Zeng

    (Institute of Frontier Interdisciplinary Technology, Chongqing Jiaotong University, Chongqing 400074, China)

  • Dongsheng Liu

    (Chongqing Bureau of Geology and Mineral Exploration and Development, Chongqing 401120, China)

  • Hao Chen

    (Institute for Geological Disaster Risk Prevention and Control in Western Region, Nanjiang Hydrogeological and Engineering Geological Team, Chongqing 401120, China)

  • Linfeng Wang

    (College of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400047, China)

  • Liping Wang

    (Laboratory of Energy Engineering Mechanics and Disaster Prevention and Mitigation, Chongqing University of Science & Technology, Chongqing 401120, China)

  • Kaiqiang Zhang

    (College of Civil Engineering, Chongqing University, Chongqing 400044, China)

  • Thomas Glade

    (Eomorphic Systems and Risk Research Unit, Department of Geography and Regional Research, University of Vienna, 1010 Vienna, Austria)

Abstract

The current method for dividing slope units primarily relies on hydrological analysis methods, which consider only geomorphological factors and fail to reveal the geological boundaries during landslides. Consequently, this approach does not fully satisfy the requirements for detailed landslide susceptibility assessments at the township scale. To address this limitation, we propose a new landslide susceptibility evaluation model based on geomorphological and geological characteristics. The key challenges addressed include: (i) Optimization of the slope unit division method. This is accomplished by integrating geomorphological features, such as slope gradient and aspect, with geological features, including lithology, slope structure types, and disaster categories, to develop a process for extracting slope units based on both geomorphological and geological characteristics. The results indicate that the proposed slope units outperform the hydrological analysis methods in three key indicators: overlap, shape regularity, and spatial distribution uniformity. (ii) Development and validation of the evaluation model. A landslide susceptibility index system is developed using multi-source data, with susceptibility prediction conducted via the XGBoost model optimized by Bayesian methods. The model’s accuracy is validated using the Receiver Operating Characteristic (ROC) curve. The results show that the proposed slope units achieve an AUC value of 0.973, surpassing the hydrological method. (iii) Analysis of landslide susceptibility variations. The susceptibility of the two types of slope units is analyzed through landslide case studies. The consistency between the proposed slope units and field verification results is explained using engineering geological characteristics. The SHAP model is then used to examine the influence of key disaster-inducing and individual factors on landslide occurrence.

Suggested Citation

  • Gang Chen & Taorui Zeng & Dongsheng Liu & Hao Chen & Linfeng Wang & Liping Wang & Kaiqiang Zhang & Thomas Glade, 2025. "Geomorphological and Geological Characteristics Slope Unit: Advancing Township-Scale Landslide Susceptibility Assessment Strategies," Land, MDPI, vol. 14(2), pages 1-43, February.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:2:p:355-:d:1587137
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/14/2/355/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/14/2/355/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ning Jia & Yasuhiro Mitani & Mowen Xie & Jianxing Tong & Zhaohui Yang, 2015. "GIS deterministic model-based 3D large-scale artificial slope stability analysis along a highway using a new slope unit division method," 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. 76(2), pages 873-890, March.
    2. Robert Emberson & Dalia Kirschbaum & Thomas Stanley, 2021. "Global connections between El Nino and landslide impacts," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    3. Patrick J. Heagerty & Yingye Zheng, 2005. "Survival Model Predictive Accuracy and ROC Curves," Biometrics, The International Biometric Society, vol. 61(1), pages 92-105, March.
    4. Langping Li & Hengxing Lan, 2020. "Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study," IJERPH, MDPI, vol. 17(21), pages 1-17, November.
    5. Hamid Reza Pourghasemi & Nitheshnirmal Sadhasivam & Mahdis Amiri & Saeedeh Eskandari & M. Santosh, 2021. "Landslide susceptibility assessment and mapping using state-of-the art machine learning techniques," 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. 108(1), pages 1291-1316, August.
    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. Batmyagmar Dashbold & L. Sebastian Bryson & Matthew M. Crawford, 2023. "Landslide hazard and susceptibility maps derived from satellite and remote sensing data using limit equilibrium analysis and machine learning model," 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. 116(1), pages 235-265, March.
    2. Yanyuan Ma & Yuanjia Wang, 2014. "Estimating disease onset distribution functions in mutation carriers with censored mixture data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(1), pages 1-23, January.
    3. Xiang Zhang & Minghui Zhang & Xin Liu & Berhanu Keno Terfa & Won-Ho Nam & Xihui Gu & Xu Zhang & Chao Wang & Jian Yang & Peng Wang & Chenghong Hu & Wenkui Wu & Nengcheng Chen, 2024. "Review on the progress and future prospects of geological disasters prediction in the era of artificial intelligence," 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(13), pages 11485-11525, October.
    4. Chuhong Shen & Kangning Xiong & Tian Shu, 2022. "Dynamic Evolution and Quantitative Attribution of Soil Erosion Based on Slope Units: A Case Study of a Karst Plateau-Gorge Area in SW China," Land, MDPI, vol. 11(8), pages 1-18, July.
    5. Pablo Mart�nez-Camblor & Jacobo de U�a-�lvarez & Carmen D�az Corte, 2015. "Expanded renal transplantation: a competing risk model approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(12), pages 2539-2553, December.
    6. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    7. Robin Van Oirbeek & Emmanuel Lesaffre, 2018. "An Investigation of the Discriminatory Ability of the Clustering Effect of the Frailty Survival Model," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 6(3), pages 87-98, April.
    8. Yuanjia Wang & Huaihou Chen & Runze Li & Naihua Duan & Roberto Lewis-Fernández, 2011. "Prediction-Based Structured Variable Selection through the Receiver Operating Characteristic Curves," Biometrics, The International Biometric Society, vol. 67(3), pages 896-905, September.
    9. Kevin He & Yue Wang & Xiang Zhou & Han Xu & Can Huang, 2019. "An improved variable selection procedure for adaptive Lasso in high-dimensional survival analysis," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 569-585, July.
    10. Raul Aguirre-Gamboa & Hugo Gomez-Rueda & Emmanuel Martínez-Ledesma & Antonio Martínez-Torteya & Rafael Chacolla-Huaringa & Alberto Rodriguez-Barrientos & José G Tamez-Peña & Victor Treviño, 2013. "SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-9, September.
    11. Weining Shen & Jing Ning & Ying Yuan, 2015. "A direct method to evaluate the time-dependent predictive accuracy for biomarkers," Biometrics, The International Biometric Society, vol. 71(2), pages 439-449, June.
    12. Matthias Schmid & Thomas Hielscher & Thomas Augustin & Olaf Gefeller, 2011. "A Robust Alternative to the Schemper–Henderson Estimator of Prediction Error," Biometrics, The International Biometric Society, vol. 67(2), pages 524-535, June.
    13. Ao Yuan & Mihai Giurcanu & George Luta & Ming T. Tan, 2017. "U-statistics with conditional kernels for incomplete data models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(2), pages 271-302, April.
    14. Dehan Kong & Joseph G. Ibrahim & Eunjee Lee & Hongtu Zhu, 2018. "FLCRM: Functional linear cox regression model," Biometrics, The International Biometric Society, vol. 74(1), pages 109-117, March.
    15. Shanshan Li & Yang Ning, 2015. "Estimation of covariate‐specific time‐dependent ROC curves in the presence of missing biomarkers," Biometrics, The International Biometric Society, vol. 71(3), pages 666-676, September.
    16. Chi Yang & Jinghan Wang & Shuyi Li & Ruihan Xiong & Xiaobo Li & Lin Gao & Xu Guo & Chuanming Ma & Hanxiang Xiong & Yang Qiu, 2024. "Landslide Susceptibility Assessment and Future Prediction with Land Use Change and Urbanization towards Sustainable Development: The Case of the Li River Valley in Yongding, China," Sustainability, MDPI, vol. 16(11), pages 1-26, May.
    17. Cullen F. Goenner, 2020. "Uncertain times and early predictions of bank failure," The Financial Review, Eastern Finance Association, vol. 55(4), pages 583-601, November.
    18. P. Saha & P. J. Heagerty, 2010. "Time-Dependent Predictive Accuracy in the Presence of Competing Risks," Biometrics, The International Biometric Society, vol. 66(4), pages 999-1011, December.
    19. Hannes Kröger & Rasmus Hoffmann, 2018. "The association between CVD-related biomarkers and mortality in the Health and Retirement Survey," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(62), pages 1933-2002.
    20. Jing Zhang & Jing Ning & Ruosha Li, 2023. "Evaluating Dynamic Discrimination Performance of Risk Prediction Models for Survival Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 353-371, 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:gam:jlands:v:14:y:2025:i:2:p:355-:d:1587137. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.