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<p>As a frequent and devastating global geological hazard, landslides pose significant threats to communities, infrastructure, and the environment. The study area in Meishan Town, China, is particularly susceptible to rainfall-induced landslides due to frequent typhoons and rainstorms. This study proposes a novel hybrid framework that integrates a Convolutional Neural Network (CNN) with the TRIGRS physical model for landslide hazard assessment under four rainfall scenarios. The core innovation is a transparent and interpretable matrix-based coupling method that combines a CNN-derived landslide susceptibility index with the Factor of Safety (Fs) computed by TRIGRS, enabling a more comprehensive hazard zonation. To address the prevalent issue of imbalanced data in landslide inventories, we optimized the CNN training by comparing SMOTE-TL and RUS sampling strategies, finding SMOTE-TL significantly superior (AUC: 0.87 vs. 0.83). Our approach, which does not require extensive pre-failure rainfall data, produces hazard maps that are strongly consistent with the historical landslide inventory. This study provides an efficient and practical solution for landslide hazard assessment, particularly in data-scarce regions.</p>

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Assessment of landslide hazard under specific rainfall scenarios based on a hybrid CNN-TRIGRS and matrix approach: a case study of Meishan Town, Zhejiang Province, China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月9日

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  • Zixuan Wang,
  • Zhongfu Wang,
  • Fengge Shi,
  • Xusheng Zhang,
  • Dan Bi,
  • Leyu Qu

摘要

As a frequent and devastating global geological hazard, landslides pose significant threats to communities, infrastructure, and the environment. The study area in Meishan Town, China, is particularly susceptible to rainfall-induced landslides due to frequent typhoons and rainstorms. This study proposes a novel hybrid framework that integrates a Convolutional Neural Network (CNN) with the TRIGRS physical model for landslide hazard assessment under four rainfall scenarios. The core innovation is a transparent and interpretable matrix-based coupling method that combines a CNN-derived landslide susceptibility index with the Factor of Safety (Fs) computed by TRIGRS, enabling a more comprehensive hazard zonation. To address the prevalent issue of imbalanced data in landslide inventories, we optimized the CNN training by comparing SMOTE-TL and RUS sampling strategies, finding SMOTE-TL significantly superior (AUC: 0.87 vs. 0.83). Our approach, which does not require extensive pre-failure rainfall data, produces hazard maps that are strongly consistent with the historical landslide inventory. This study provides an efficient and practical solution for landslide hazard assessment, particularly in data-scarce regions.