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

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  • Kangcheng Zhu

    (School of Ecology and Environment, Xizang University, Lhasa 850000, China
    These authors contributed equally to this work.)

  • Sen Hu

    (School of Ecology and Environment, Xizang University, Lhasa 850000, China
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Kangcheng Zhu & Sen Hu & Yuzhong Kong & Jianwei Zhou & Junzhe Teng & Weiyan Luo & Jihang Li & Yang Pu & Taijin Su & Junmeng Zhao & Zhen Jiang, 2025. "Enhancing Sustainable Disaster Risk Management: Landslide Susceptibility Evaluation Using AdaBoost-CB Ensemble and Multi-Dimensional Vegetation Metrics in Yuanling County, China," Sustainability, MDPI, vol. 17(21), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:21:p:9358-:d:1776924
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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Saddam Hossen & M. Salim Uddin & Yaqub Ali & Parvez Rana, 2025. "Integrated analysis of land use and land cover changes and landslide susceptibility: a machine learning approach in Rangamati Sadar, Bangladesh," 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. 121(16), pages 19387-19408, September.
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