干旱区地理2026，Vol.49Issue(9)：1788-1802,15.DOI:10.12118/j.issn.1000-6060.2025.641
中巴公路盖孜河谷段形变监测与滑坡易发性评价方法
Deformation monitoring and landslide susceptibility assessment method for the Gaizi River Valley of the China-Pakistan highway
摘要
Abstract
Landslide susceptibility mapping(LSM)is a critical component of landslide hazard risk assessment.To address the limitations of existing models in multi-scale feature extraction,a hybrid feature extraction net-work,termed multi-scale attention depthwise separable convolution with a channel-prioritized Transformer net-work(MADTNet)was developed.This approach effectively captures both local and global landslide features,en-abling more accurate LSM.The Gaizi River Valley section of the China-Pakistan highway was selected as the study area.To construct a comprehensive dataset for modeling landslide susceptibility,surface deformation data collected from January 2019 to June 2024 were retrieved using a small baseline subset interferometric synthetic aperture radar(SBAS-InSAR)technique and incorporated as a dynamic factor,along with ten static factors,in-cluding topography,geological conditions,hydrological characteristics,and human activities.The performance of MADTNet was systematically compared with that of five benchmark deep learning models convolutional neural network(CNN),residual network(ResNet),vision Transformer(ViT),fractal-based information Transformer(FrIT),and Transformer-based U-net(TransUNet).The results indicate that:(1)The line-of-sight deformation rates in the study area range from-52.5 mm·a-1 to 19.4 mm·a-1,while the slope-direction deformation rates range from-70.6 mm·a-1 to 25.7 mm·a-1.(2)MADTNet achieves the best overall performance,with an area-un-der-the-curve of 0.959,demonstrating superior predictive accuracy and generalization capability compared to the other models.(3)Shapley additive explanations(SHAP)analysis reveals that the distance to rivers,distance to roads,slope,and elevation difference are the dominant factors influencing landslide development.Overall,MADTNet achieves high-precision and interpretable landslide susceptibility predictions,offering robust scientific support for targeted landslide mitigation and disaster risk management.关键词
喀喇昆仑公路/InSAR/CNN/Transformer/滑坡易发性评价/SHAP分析Key words
Karakoram highway/InSAR/CNN/Transformer/landslide susceptibility evaluation/SHAP analysis引用本文复制引用
王院荣,火久元,张耀南..中巴公路盖孜河谷段形变监测与滑坡易发性评价方法[J].干旱区地理,2026,49(9):1788-1802,15.基金项目
国家冰川冻土沙漠科学数据中心数据专题项目资助 （）
