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<p>Landslide susceptibility assessment serves as a critical component in landslide hazard prevention and mitigation. With advancements in data-driven technologies, machine learning models have gained increasing prominence in this field. This study retrieved 817 peer-reviewed articles from the Web of Science Core Collection (WOSCC) and employed CiteSpace and VOSviewer for data processing and visualization to systematically investigate research progress in machine learning-driven landslide susceptibility assessment from 2009 to 2024. The results show that the number of publications in this field shows a continuous growth trend, with publications in 2024 accounting for 28% of the total number of publications. Collaborative relationships between authors were significantly strengthened after 2016, especially after 2021, showing a multi-center and high-density cooperation pattern. China dominates global scholarly output in this field with 437 publications, while forming cooperative relationships with many countries around the world. Keyword cooccurrence analysis and temporal analysis reveal a hotspot shift from traditional models to complex methods such as deep learning and ensemble learning and have begun to pay attention to model interpretability. Journals including <i>Engineering Geology, Computers &amp; Geosciences, Science of the Total Environment, Geomorphology</i>, and <i>Landslides</i> exhibit high average co-citation frequencies, underscoring their status as platforms for high-impact research. Deep learning and ensemble learning have received increasing attention in landslide susceptibility assessment. Convolutional neural networks are increasingly seen as a promising direction for future research. Overall, this study provides an overview of the development trends, knowledge structure, and emerging research directions in machine learning-driven landslide susceptibility assessment, offering valuable insights for future research and methodological advancement.</p>

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Research progress in landslide susceptibility assessment driven by machine learning: A bibliometric analysis

  • 期刊论文

  • 发表日期：

    2026年4月28日

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  • Tao Li,
  • Chong Xu

摘要

Landslide susceptibility assessment serves as a critical component in landslide hazard prevention and mitigation. With advancements in data-driven technologies, machine learning models have gained increasing prominence in this field. This study retrieved 817 peer-reviewed articles from the Web of Science Core Collection (WOSCC) and employed CiteSpace and VOSviewer for data processing and visualization to systematically investigate research progress in machine learning-driven landslide susceptibility assessment from 2009 to 2024. The results show that the number of publications in this field shows a continuous growth trend, with publications in 2024 accounting for 28% of the total number of publications. Collaborative relationships between authors were significantly strengthened after 2016, especially after 2021, showing a multi-center and high-density cooperation pattern. China dominates global scholarly output in this field with 437 publications, while forming cooperative relationships with many countries around the world. Keyword cooccurrence analysis and temporal analysis reveal a hotspot shift from traditional models to complex methods such as deep learning and ensemble learning and have begun to pay attention to model interpretability. Journals including Engineering Geology, Computers & Geosciences, Science of the Total Environment, Geomorphology, and Landslides exhibit high average co-citation frequencies, underscoring their status as platforms for high-impact research. Deep learning and ensemble learning have received increasing attention in landslide susceptibility assessment. Convolutional neural networks are increasingly seen as a promising direction for future research. Overall, this study provides an overview of the development trends, knowledge structure, and emerging research directions in machine learning-driven landslide susceptibility assessment, offering valuable insights for future research and methodological advancement.