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    Volume 50 Issue 10
    Oct.  2025
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    Peng Ming, Wang Yue, Ma Chenyi, Shi Zhenming, Zhu Yan, 2025. Review of Risk Assessment and Prevention for Valley Landslide Disaster Chains. Earth Science, 50(10): 3723-3760. doi: 10.3799/dqkx.2025.142
    Citation: Peng Ming, Wang Yue, Ma Chenyi, Shi Zhenming, Zhu Yan, 2025. Review of Risk Assessment and Prevention for Valley Landslide Disaster Chains. Earth Science, 50(10): 3723-3760. doi: 10.3799/dqkx.2025.142

    Review of Risk Assessment and Prevention for Valley Landslide Disaster Chains

    doi: 10.3799/dqkx.2025.142
    • Received Date: 2025-05-02
    • Publish Date: 2025-10-25
    • The valley landslide disaster chain refers to a series of cascading hazardous events resulting from the complex interactions between landslide masses in river-adjacent areas and fluvial systems among the most common types are landslide-induced tsunami and landslide damming disaster chains. Research indicates that the uncertainties in valley landslide disaster chains mainly stem from three aspects: the uncertainty of disaster-triggering mechanisms, the dynamic unpredictability of the movement processes, and the coupling uncertainty in chain interactions. Current methods for disaster chain identification and susceptibility assessment primarily utilize remote sensing, spatial analysis, and machine learning techniques. However, these approaches often fail to adequately account for cascading effects and spatiotemporal evolution characteristics. Although qualitative, quantitative, and numerical simulation-based methods have been developed for risk assessment, limitations remain due to data scarcity and the lack of large-scale mechanistic analysis tools. Current methodologies often couple individual segments of the disaster chain, but fail to capture its systemic integrity and cascading interactions. Although combined structural and non-structural mitigation measures have been implemented, quantitative evaluations of chain-breaking effectiveness remain underdeveloped. Future research should focus on: developing multi-physics, cross-scale evolution theories of disaster chains; establish a large-scale, continuous multi-field monitoring and multi-source heterogeneous data fusion identification system; constructing a data- and physics-driven full-process risk assessment model for disaster chains; and strengthening precise early warning and systematic chain-breaking mitigation strategies.

       

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