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<p>The occurrence and intensity of landslides are rising globally due to climate change, posing threats to both people and infrastructure. As a result, assessing landslide vulnerability has become increasingly critical. Besides that, the simultaneous occurrence of rainfall and seismic activity significantly raises the likelihood of landslides. However, limited research has been conducted on this specific aspect. Therefore, this study introduces an innovative framework for evaluating the regional vulnerability of landslides induced by both rainfall and earthquakes at the case study site of Mt. Umyeon, Korea. The proposed framework includes three key steps: (1) analyzing landslide hazards using a combination of machine learning and physical models, (2) assessing landslide runout propagation process based on Flow-R, and (3) computing a vulnerability index for each assessed area by integrating physical vulnerability and quantitative approaches. To ensure the reliability of the framework, its results were validated using a landslide event that occurred on Mt. Umyeon in July 2011. The framework effectively identified landslide vulnerability in the study area. This approach enhances the accuracy and comprehensiveness of landslide vulnerability assessment and supports informed decision-making for risk management.</p>

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Assessing residential buildings vulnerability on a regional scale to rainfall–earthquake-induced landslides on Mt. Umyeon, Korea

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Ba-Quang-Vinh Nguyen,
  • Tan-Hung Nguyen

摘要

The occurrence and intensity of landslides are rising globally due to climate change, posing threats to both people and infrastructure. As a result, assessing landslide vulnerability has become increasingly critical. Besides that, the simultaneous occurrence of rainfall and seismic activity significantly raises the likelihood of landslides. However, limited research has been conducted on this specific aspect. Therefore, this study introduces an innovative framework for evaluating the regional vulnerability of landslides induced by both rainfall and earthquakes at the case study site of Mt. Umyeon, Korea. The proposed framework includes three key steps: (1) analyzing landslide hazards using a combination of machine learning and physical models, (2) assessing landslide runout propagation process based on Flow-R, and (3) computing a vulnerability index for each assessed area by integrating physical vulnerability and quantitative approaches. To ensure the reliability of the framework, its results were validated using a landslide event that occurred on Mt. Umyeon in July 2011. The framework effectively identified landslide vulnerability in the study area. This approach enhances the accuracy and comprehensiveness of landslide vulnerability assessment and supports informed decision-making for risk management.