Scholarlink

<p>Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau (QTP), endangering both ecosystems and human life. Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk. This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest (RF), Gradient Boosting Regression Trees (GBRT), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—to generate susceptibility maps. The Shapley additive explanation (SHAP) method was applied to quantify factor importance and explore their nonlinear effects. The results showed that: (1) CatBoost was the best-performing model (CA=0.938, AUC=0.980) in assessing landslide susceptibility, with altitude emerging as the most significant factor, followed by distance to roads and earthquake sites, precipitation, and slope; (2) the SHAP method revealed critical nonlinear thresholds, demonstrating that historical landslides were concentrated at mid-altitudes (1400–4000 m) and decreased markedly above 4000 m, with a parallel reduction in probability beyond 700 m from roads; and (3) landslide-prone areas, comprising 13% of the QTP, were concentrated in the southeastern and northeastern parts of the plateau. By integrating machine learning and SHAP analysis, this study revealed landslide hazard-prone areas and their driving factors, providing insights to support disaster management strategies and sustainable regional planning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Landslide susceptibility on the Qinghai-Tibet Plateau: Key driving factors identified through machine learning

  • 期刊论文

  • 发表日期：

    2026年1月28日

查看全文
  • Wanqing Yang,
  • Quansheng Ge,
  • Zexing Tao,
  • Duanyang Xu,
  • Yuan Wang,
  • Zhixin Hao

摘要

Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau (QTP), endangering both ecosystems and human life. Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk. This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest (RF), Gradient Boosting Regression Trees (GBRT), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—to generate susceptibility maps. The Shapley additive explanation (SHAP) method was applied to quantify factor importance and explore their nonlinear effects. The results showed that: (1) CatBoost was the best-performing model (CA=0.938, AUC=0.980) in assessing landslide susceptibility, with altitude emerging as the most significant factor, followed by distance to roads and earthquake sites, precipitation, and slope; (2) the SHAP method revealed critical nonlinear thresholds, demonstrating that historical landslides were concentrated at mid-altitudes (1400–4000 m) and decreased markedly above 4000 m, with a parallel reduction in probability beyond 700 m from roads; and (3) landslide-prone areas, comprising 13% of the QTP, were concentrated in the southeastern and northeastern parts of the plateau. By integrating machine learning and SHAP analysis, this study revealed landslide hazard-prone areas and their driving factors, providing insights to support disaster management strategies and sustainable regional planning.