Enhancing Sustainable Disaster Risk Management: Landslide Susceptibility Evaluation Using AdaBoost-CB Ensemble and Multi-Dimensional Vegetation Metrics in Yuanling County, China

Kangcheng Zhu, Sen Hu, Yuzhong Kong, Jianwei Zhou, Junzhe Teng, Weiyan Luo, Jihang Li, Yang Pu, Taijin Su, Junmeng Zhao () and Zhen Jiang
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Kangcheng Zhu: School of Ecology and Environment, Xizang University, Lhasa 850000, China
Sen Hu: School of Ecology and Environment, Xizang University, Lhasa 850000, China
Yuzhong Kong: School of Engineering, Xizang University, Lhasa 850000, China
Jianwei Zhou: School of Ecology and Environment, Xizang University, Lhasa 850000, China
Junzhe Teng: School of Ecology and Environment, Xizang University, Lhasa 850000, China
Weiyan Luo: School of Ecology and Environment, Xizang University, Lhasa 850000, China
Jihang Li: School of Ecology and Environment, Xizang University, Lhasa 850000, China
Yang Pu: School of Ecology and Environment, Xizang University, Lhasa 850000, China
Taijin Su: School of Engineering, Xizang University, Lhasa 850000, China
Junmeng Zhao: School of Science, Xizang University, Lhasa 850000, China
Zhen Jiang: School of Ecology and Environment, Xizang University, Lhasa 850000, China

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

Abstract: Landslides pose significant threats to sustainable development by causing infrastructure damage and ecosystem degradation, particularly in densely vegetated mountainous regions. To support sustainable land-use planning and disaster-resilient development, this study integrates three advanced vegetation metrics—Vegetation Formation Group (VFG), aboveground biomass (AGB), and forest canopy height (FCH)—into landslide susceptibility modeling. Using Yuanling County, a subtropical vegetated region in China, as a case study, we developed a novel ensemble model, AdaBoost-CB (AdaBoost-CatBoost), and compared its performance with mainstream machine learning models including RF, XGBoost, and LGB. The results show that AdaBoost-CB achieved the highest Area Under the Curve (AUC) value of 0.915. Furthermore, it yielded the highest landslide frequency ratio of 6.51 in the very-high-susceptibility zones. The dominant landslide-controlling factors—NDVI, elevation, slope gradient, slope aspect, and rainfall—were consistently identified across six models. These findings provide a scientific basis for sustainable land-use planning and disaster risk reduction strategies, contributing directly to the goals of sustainable development in vulnerable mountainous regions.

Keywords: landslide susceptibility assessment; vegetation factors; ensemble machine learning; Adaboost-CB ensemble; sustainable disaster risk management; land-use planning (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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