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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
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    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.
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    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.
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