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<p>Co-seismic landslides pose significant risks in seismically active regions, making accurate susceptibility mapping essential for effective disaster preparedness, management, and risk reduction. This state-of-the-art review offers a summary of the existing quantitative and semi-quantitative strategies for co-seismic landslide susceptibility (CLS) mapping. It includes a statistical analysis of 247 publications published between 1994 and 2023. The review discusses various approaches, focusing on statistical, semi-quantitative, and machine learning (ML) techniques. Quantitative methods, such as Newmark’s model, are widely used, alongside qualitative and semi-quantitative methods that rely on expert judgment and scoring systems. Key factors influencing landslide susceptibility, such as geological, geomorphological, hydrological, and seismological, are explored, highlighting their importance in accurate mapping. The study also emphasises the role of remote sensing (RS) and geographic information systems (GIS) technologies in improving the resolution and efficiency of CLS mapping. However, challenges remain, including the lack of comprehensive data on soil and rock parameters, the complexity of landslide behaviour, and difficulties in earthquake prediction. The review highlights the need for improved landslide inventories, optimal selection of causal factors, and robust validation techniques to enhance the reliability of CLS maps. Additionally, the manuscript stresses the growing significance of ML techniques and their potential to address uncertainties in mapping. This review serves as a valuable resource for researchers, engineers, and policymakers working to assess seismic hazards and mitigate landslide risks in earthquake-prone areas, urging continued development and refinement of CLS methodologies.</p>

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Mapping Co-seismic Landslide Susceptibility: An Overview of Current and Emerging Methods

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Kunal Gupta,
  • Neelima Satyam,
  • Falak,
  • Sahiya Rahim

摘要

Co-seismic landslides pose significant risks in seismically active regions, making accurate susceptibility mapping essential for effective disaster preparedness, management, and risk reduction. This state-of-the-art review offers a summary of the existing quantitative and semi-quantitative strategies for co-seismic landslide susceptibility (CLS) mapping. It includes a statistical analysis of 247 publications published between 1994 and 2023. The review discusses various approaches, focusing on statistical, semi-quantitative, and machine learning (ML) techniques. Quantitative methods, such as Newmark’s model, are widely used, alongside qualitative and semi-quantitative methods that rely on expert judgment and scoring systems. Key factors influencing landslide susceptibility, such as geological, geomorphological, hydrological, and seismological, are explored, highlighting their importance in accurate mapping. The study also emphasises the role of remote sensing (RS) and geographic information systems (GIS) technologies in improving the resolution and efficiency of CLS mapping. However, challenges remain, including the lack of comprehensive data on soil and rock parameters, the complexity of landslide behaviour, and difficulties in earthquake prediction. The review highlights the need for improved landslide inventories, optimal selection of causal factors, and robust validation techniques to enhance the reliability of CLS maps. Additionally, the manuscript stresses the growing significance of ML techniques and their potential to address uncertainties in mapping. This review serves as a valuable resource for researchers, engineers, and policymakers working to assess seismic hazards and mitigate landslide risks in earthquake-prone areas, urging continued development and refinement of CLS methodologies.