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<p>As one of the most representative hydropower project areas in China and globally, the Three Gorges Reservoir Area (TGRA) has attracted significant attention due to its complex geological setting and frequent geological hazards. This study provides a systematic review of recent advances in landslide assessment within the TGRA, outlining the technological evolution from traditional limit equilibrium methods, numerical simulations, and physical model tests to the integration of big data and machine learning approaches. The theoretical foundations, application effectiveness, and limitations of each method are analyzed in detail. The findings indicate that the deep integration of machine learning and remote sensing technologies has significantly improved the spatial and temporal resolution of landslide prediction. Frontier approaches, such as ensemble models (e.g., Stacking, XGBoost) and physics-informed neural networks (PINN), have demonstrated considerable potential. However, challenges persist, including limited data quality, insufficient model generalization, lag in dynamic assessment, and inadequate quantification of climate change impacts. In response, this paper proposes a four-tier theoretical framework encompassing stability–susceptibility–hazard–risk, which elucidates the technical linkages and integration pathways for multi-scale assessments. Future research directions are proposed with a focus on dynamic monitoring, mechanism-informed modeling, and climate adaptation. These findings aim to provide a scientific basis for landslide disaster mitigation and risk management in the TGRA and offer theoretical support for regional geological hazard control and sustainable development.</p>

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Landslide assessment research in the three gorges reservoir area: A review of methodological advances and future directions

  • 期刊论文

  • 发表日期：

    2026年1月16日

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  • Yuandong Huang,
  • Chong Xu,
  • Xiaoyi Shao,
  • Xiangli He,
  • Zikang Xiao,
  • Xiwei Xu,
  • Yawei Xie,
  • Xuning Nie,
  • Xin Li

摘要

As one of the most representative hydropower project areas in China and globally, the Three Gorges Reservoir Area (TGRA) has attracted significant attention due to its complex geological setting and frequent geological hazards. This study provides a systematic review of recent advances in landslide assessment within the TGRA, outlining the technological evolution from traditional limit equilibrium methods, numerical simulations, and physical model tests to the integration of big data and machine learning approaches. The theoretical foundations, application effectiveness, and limitations of each method are analyzed in detail. The findings indicate that the deep integration of machine learning and remote sensing technologies has significantly improved the spatial and temporal resolution of landslide prediction. Frontier approaches, such as ensemble models (e.g., Stacking, XGBoost) and physics-informed neural networks (PINN), have demonstrated considerable potential. However, challenges persist, including limited data quality, insufficient model generalization, lag in dynamic assessment, and inadequate quantification of climate change impacts. In response, this paper proposes a four-tier theoretical framework encompassing stability–susceptibility–hazard–risk, which elucidates the technical linkages and integration pathways for multi-scale assessments. Future research directions are proposed with a focus on dynamic monitoring, mechanism-informed modeling, and climate adaptation. These findings aim to provide a scientific basis for landslide disaster mitigation and risk management in the TGRA and offer theoretical support for regional geological hazard control and sustainable development.