成果归属机构：

环境与生态前沿交叉研究院

作者：

Pan,Mingchen¹ ;Zhang,Hong¹ ;Sun,Deliang¹ ;Zhang,Qiang(张强) ² ;Zhu,Xing³ ;Wen,Haijia⁴ ;Zhao,Jianjun⁵ ;Tang,Yunhui⁶ ;Zhang,Tingbin⁷

单位：

[1]Chongqing Normal Univ, GIS Applicat Res Key Lab Chongqing Educ Commiss Ch, Chongqing, Peoples R China;[2]Beijing Normal Univ, Adv Interdisciplinary Inst Environm & Ecol, Guangdong Prov Key Lab Wastewater Informat Anal &, Zhuhai, Peoples R China;[3]Chengdu Univ Technol, Coll Environm & Civil Engn, Chengdu, Peoples R China;[4]Chongqing Univ, Key Lab New Technol Construct Cities Mt Area, Chongqing, Peoples R China;[5]Chengdu Univ Technol, State Key Lab Geohazard Prevent & Geoenvironm Prot, Chengdu, Peoples R China;[6]Chongqing Inst Meteorol Sci, Chongqing, Peoples R China;[7]Chengdu Univ Technol, Coll Earth Sci, Chengdu, Peoples R China

关键词：

PREDICTION

摘要：

Spatial heterogeneity poses a significant challenge to large-scale landslide susceptibility assessment, often leading to generalized models that overlook local variations. Focusing on southeastern China, a region with complex terrain, this study integrates a geomorphological zoning strategy with the XGBoost algorithm to enhance prediction accuracy. The study area was partitioned into two distinct sub-regions based on environmental disparities, utilizing 14 conditioning factors. The comparative analysis reveals that the sub-region II model achieved superior performance with an AUC of 97.44%, significantly surpassing both the sub-region I model (85.86%) and the overall model (89.74%). This provides compelling evidence that the zoning strategy outperforms global modeling by eschewing the homogenization of complex terrain, thus precisely delineating local disaster mechanisms. Furthermore, SHAP analysis elucidated that dominant drivers are divergent across sub-regions, driven by unique local geographical contexts. Consequently, coupling regional partitioning with interpretable machine learning effectively captures local disaster mechanisms, providing a robust and precise scientific basis for disaster prevention and risk management.

基金：

Natural Science Foundation of Chongqing [Grant No. CSTB2023NSCQ-MSX0618]; National Natural Science Foundation of China [Grant No. 41807498]; Chongqing Municipal Education Commission [Grant Nos. KJZD-K202300511, YIG233075]

语种：

英文

DOI：

10.1080/19475705.2026.2635669

来源：

GEOMATICS NATURAL HAZARDS & RISK,2026(1):.

出版日期：

2026-12-31

提交日期：

2026-03-18

引用参考：

Pan,Mingchen;Zhang,Hong;Sun,Deliang;Zhang,Qiang;Zhu,Xing;Wen,Haijia;Zhao,Jianjun;Tang,Yunhui;Zhang,Tingbin. A comparative study on spatial patterns and dominant factors of landslide susceptibility under geomorphological differentiation[J]. GEOMATICS NATURAL HAZARDS & RISK,2026(1):.