Landslide susceptibility on the Qinghai-Tibet Plateau: Key driving factors identified through machine learning
文献类型：期刊论文
| 作者 | Yang, Wanqing1,2; Ge, Quansheng1,2; Tao, Zexing1; Xu, Duanyang1; Wang, Yuan1; Hao, Zhixin1,2 |
| 刊名 | JOURNAL OF GEOGRAPHICAL SCIENCES
 |
| 出版日期 | 2026 |
| 卷号 | 36期号:1页码:199-218 |
| 关键词 | landslide susceptibility machine learning SHAP driving factors nonlinear effects |
| ISSN号 | 1009-637X |
| DOI | 10.1007/s11442-026-2444-6 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| URL标识 | 查看原文 |
| WOS关键词 | FREQUENCY RATIO ; FAULT ZONE ; HYPERPARAMETER OPTIMIZATION ; DATASET ; EARTHQUAKE ; MOUNTAINS ; REGION ; CHINA ; MODEL ; RIVER |
| WOS研究方向 | Physical Geography |
| 语种 | 英语 |
| WOS记录号 | WOS:001673292900003 |
| 出版者 | SCIENCE PRESS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221058]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Hao, Zhixin |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Wanqing,Ge, Quansheng,Tao, Zexing,et al. Landslide susceptibility on the Qinghai-Tibet Plateau: Key driving factors identified through machine learning[J]. JOURNAL OF GEOGRAPHICAL SCIENCES,2026,36(1):199-218. |
| APA | Yang, Wanqing,Ge, Quansheng,Tao, Zexing,Xu, Duanyang,Wang, Yuan,&Hao, Zhixin.(2026).Landslide susceptibility on the Qinghai-Tibet Plateau: Key driving factors identified through machine learning.JOURNAL OF GEOGRAPHICAL SCIENCES,36(1),199-218. |
| MLA | Yang, Wanqing,et al."Landslide susceptibility on the Qinghai-Tibet Plateau: Key driving factors identified through machine learning".JOURNAL OF GEOGRAPHICAL SCIENCES 36.1(2026):199-218. |
入库方式： OAI收割
来源：地理科学与资源研究所
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