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<p>Geological hazards, such as landslides, pose significant risks to railway infrastructure. This study assesses landslide susceptibility along a critical section of the Hefei-Fuzhou High-Speed Railway, which traverses a geologically complex region. We developed a landslide susceptibility model using the Random Forest (RF) algorithm based on a historical landslide inventory. The model demonstrates excellent predictive performance, and the resulting susceptibility map highlights that a significant portion of the study area falls within the high and extremely high susceptibility zones. These high-risk areas are spatially correlated with steep slopes and high annual precipitation, providing crucial guidance for regional disaster prevention. Furthermore, an assessment of influencing factor importance using the RF algorithm revealed that slope is the dominant factor affecting landslide susceptibility. The varying importance rankings of other factors between the northern and southern sub-regions highlight the complex and diverse landslide mechanisms driven by different geological conditions. This research fills a key gap in geological hazard mapping for a major high-speed railway corridor in southeastern China. The findings and methodological approach offer a valuable framework for regional infrastructure planning and geological risk management in similar developing regions.</p>

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Evaluation of geological hazards susceptibility along a key railway based on machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

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  • Jiarong Liang,
  • Wenwen Qi,
  • Chong Xu,
  • Peng Wang,
  • Jingjing Sun,
  • Xuewei Zhang,
  • Zhiwen Xue,
  • Jingyu Chen,
  • Yulong Cui,
  • Junwei Pan,
  • Juanling Wang,
  • Qihao Sun

摘要

Geological hazards, such as landslides, pose significant risks to railway infrastructure. This study assesses landslide susceptibility along a critical section of the Hefei-Fuzhou High-Speed Railway, which traverses a geologically complex region. We developed a landslide susceptibility model using the Random Forest (RF) algorithm based on a historical landslide inventory. The model demonstrates excellent predictive performance, and the resulting susceptibility map highlights that a significant portion of the study area falls within the high and extremely high susceptibility zones. These high-risk areas are spatially correlated with steep slopes and high annual precipitation, providing crucial guidance for regional disaster prevention. Furthermore, an assessment of influencing factor importance using the RF algorithm revealed that slope is the dominant factor affecting landslide susceptibility. The varying importance rankings of other factors between the northern and southern sub-regions highlight the complex and diverse landslide mechanisms driven by different geological conditions. This research fills a key gap in geological hazard mapping for a major high-speed railway corridor in southeastern China. The findings and methodological approach offer a valuable framework for regional infrastructure planning and geological risk management in similar developing regions.