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

Gang Chen, Taorui Zeng (), Dongsheng Liu, Hao Chen, Linfeng Wang, Liping Wang, Kaiqiang Zhang and Thomas Glade
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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

Land, 2025, vol. 14, issue 2, 1-43

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.

Keywords: geomorphological and geological characteristics slope unit; landslide susceptibility; XGBoost; township scale (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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