Towards Sustainable Development: Landslide Susceptibility Assessment with Sample Optimization in Guiyang County, China

Yuzhong Kong, Kangcheng Zhu, Hua Wu (), Chong Xu, Ze Meng, Hui Kong, Wen Tan, Xiangyun Kong, Xingwang Chen, Linna Chen and Tong Xu
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Yuzhong Kong: School of Engineering, Xi Zang University, Lhasa 850032, China
Kangcheng Zhu: Bomi Geological Hazards Field Scientific Observation and Research Station of the Ministry of Education, Bomê, Nyingchi 860300, China
Hua Wu: School of Engineering, Xi Zang University, Lhasa 850032, China
Chong Xu: National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Ze Meng: School of Engineering, Xi Zang University, Lhasa 850032, China
Hui Kong: Geospatial Survey and Monitoring Institute of Hunan Province, Changsha 410129, China
Wen Tan: Geospatial Survey and Monitoring Institute of Hunan Province, Changsha 410129, China
Xiangyun Kong: School of Engineering, Xi Zang University, Lhasa 850032, China
Xingwang Chen: School of Engineering, Xi Zang University, Lhasa 850032, China
Linna Chen: School of Engineering, Xi Zang University, Lhasa 850032, China
Tong Xu: College of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, China

Sustainability, 2025, vol. 17, issue 21, 1-31

Abstract: Here we present a high-resolution landslide susceptibility model for Guiyang County, China, developed to support sustainable disaster risk management. Our approach couples optimized positive and negative training samples with an ensemble of machine-learning algorithms to maximize predictive fidelity. We compiled a georeferenced inventory of 146 landslides by integrating historical records with systematic field validation. Sample optimization was central to our methodology: landslide presence points were refined via buffer-based dilution, and four classifiers—SVM, LDA, RF, and ET—were trained with identical covariate sets to ensure comparability. Three strategies for selecting pseudo-absences—buffering, low-slope filtering, and coupling with the IOE—were benchmarked. The Slope-IOE-O model, which synergizes low-gradient screening with entropy-weighted sampling, yielded the highest predictive capacity (AUC = 0.965). SHAP-based interpretability revealed that slope, monthly maximum rainfall, surface roughness, and elevation collectively dominate susceptibility, with pronounced non-linearities and interactions. Slope contribution peaks at 20–30°, monthly maximum rainfall exhibits a critical threshold near 225 mm, and the synergy between high roughness and road density amplifies landslide risk. Spatially, susceptibility follows a pronounced north–south gradient, with high-hazard corridors aligned along northern and southern mountain belts and the urban core of southern Guiyang County. By integrating rigorously curated training data with robust machine-learning workflows, this study provides a transferable framework for proactive landslide risk assessment, offering scientific support for sustainable land-use planning and resilient development in mountainous regions.

Keywords: landslide susceptibility assessment; sample optimization; machine learning; Guiyang County; SHAP; sustainable disaster-risk reduction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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