An integrated landslide susceptibility assessment in the Karakoram Mountains based on SBAS-InSAR and machine learning: a case study of the Hunza Valley

IF 3.7 2区工程技术 Q3 ENGINEERING, ENVIRONMENTAL

Bulletin of Engineering Geology and the Environment Pub Date : 2025-05-07 DOI:10.1007/s10064-025-04299-8

Xiaojun Su, Yi Zhang, Xingmin Meng, Mohib Ur Rehman, Dongxia Yue, Yan Zhao, Ziqiang Zhou, Fuyun Guo, Qiang Zhou, Baicheng Niu

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引用次数: 0

Abstract

Landslide caused catastrophic disasters frequently in the Karakoram Mountains wide range. The Hunza Valley, Pakistan in the Northwest of the Karakoram mountains, which is prone to the clustering development of landslide was taken as a case in this study. The updated complete inventory including 53 SBAS-InSAR detected active landslides and optical image interpreted 65 landslides were constructed, based on Sentinel-1A data in 2019–2020 and several field survey until 2023. Twelve factors related to geomorphology, hydrology, vegetation, geology, tectonics, and environment were incorporated into the model training within twelve machine learning models: Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines and so on. The Support Vector Classification was selected for landslide susceptibility mapping (LSM) and its characteristics in geomorphologically meaningful landscape partitions called slope units, with the highest accuracy of 0.96, average AUC of 0.99 for tenfold cross-validation, and high computational efficiency of 6.11 s. The results revealed that the areas with moderate landslide susceptibility account for 62.14%, followed by the high susceptibility area (24.25%). The slopes of high landslide susceptibility are mainly located on the north side of the Hunza river, because of higher terrain relief, aspect, near distance to epicenters, and to fault. This research reveals that topography and tectonic activities such as relief, aspect, earthquakes and fault movements make a large contribution to landslide formation and development in Hunza, the Karakoram mountains, serving as a crucial step toward understanding and facilitating hazard management and risk reduction in the Hunza Valley and thus the uninterrupted operation of the KKH.

(1) An integrated method for landslide susceptibility assessment in high mountainous areas of the Karakoram in High Mountain Asia.

(2) A state-of-the-art comprehensive landslide inventory using SBAS-InSAR monitoring, optical image interpretation and field investigation was completed in Hunza Valley, Pakistan.

(3) Comprehensive selection and application of machine learning model and inducing factors in the Hunza Valley, Northern Pakistan.

(4) Revealed the factors controlling the landslides susceptibility and its characteristics in Hunza Valley, in the Karakoram Mountains

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基于SBAS-InSAR和机器学习的喀喇昆仑山区滑坡易感性综合评价——以罕萨河谷为例

山体滑坡在喀喇昆仑山脉广泛的范围内频繁造成灾难性灾害。本文以喀喇昆仑山脉西北部巴基斯坦罕萨河谷为例，研究了该地区滑坡群聚发展的易发地区。基于2019-2020年的Sentinel-1A数据和2023年之前的几次实地调查，构建了更新的完整清单，包括53个SBAS-InSAR探测到的活跃滑坡和65个光学图像解释的滑坡。将与地貌、水文、植被、地质、构造和环境相关的12个因素纳入到12个机器学习模型中的模型训练中：广义线性模型、海军贝叶斯、最近邻、支持向量机等。在具有地貌意义的景观分区（即坡单元）中，选择支持向量分类（Support Vector Classification）进行滑坡易感性制图（LSM）及其特征，其最高精度为0.96，十倍交叉验证的平均AUC为0.99，计算效率高达6.11 s。结果表明：滑坡中等易感性区占62.14%，其次是高易感性区（24.25%）；高滑坡易感性边坡主要分布在罕萨河北侧，地形起伏较大，坡度较大，离震中较近，离断层较近。研究表明，地形和构造活动（起伏、坡向、地震和断层运动）对喀喇昆仑罕萨地区滑坡的形成和发展起着重要作用。(1)建立了喀喇昆仑高原高山区滑坡易感性综合评价方法。(2)利用SBAS-InSAR监测、光学图像解译和野外调查，在罕萨河谷完成了最先进的滑坡综合清查。(3)机器学习模型与诱发因素在巴基斯坦北部罕萨河谷的综合选择与应用。(4)揭示喀喇昆仑山脉罕萨河谷滑坡易感性的控制因素及其特征

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来源期刊

Bulletin of Engineering Geology and the Environment 工程技术-地球科学综合

CiteScore

7.10

自引率

11.90%

发文量

445

审稿时长

4.1 months

期刊介绍： Engineering geology is defined in the statutes of the IAEG as the science devoted to the investigation, study and solution of engineering and environmental problems which may arise as the result of the interaction between geology and the works or activities of man, as well as of the prediction of and development of measures for the prevention or remediation of geological hazards. Engineering geology embraces: • the applications/implications of the geomorphology, structural geology, and hydrogeological conditions of geological formations; • the characterisation of the mineralogical, physico-geomechanical, chemical and hydraulic properties of all earth materials involved in construction, resource recovery and environmental change; • the assessment of the mechanical and hydrological behaviour of soil and rock masses; • the prediction of changes to the above properties with time; • the determination of the parameters to be considered in the stability analysis of engineering works and earth masses.