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SBAS-InSAR based validated landslide susceptibility mapping along the Karakoram Highway: a case study of Gilgit-Baltistan, Pakistan.
Kulsoom, Isma; Hua, Weihua; Hussain, Sadaqat; Chen, Qihao; Khan, Garee; Shihao, Dai.
Afiliação
  • Kulsoom I; School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China.
  • Hua W; School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China. huaweihua@cug.edu.cn.
  • Hussain S; Department of Geological Engineering, University of Engineering and Technology, (Lahore), Lahore, 54890, Pakistan.
  • Chen Q; School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China.
  • Khan G; Department of Earth Sciences, Karakoram International University, Gilgit, 15100, Pakistan.
  • Shihao D; School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China.
Sci Rep ; 13(1): 3344, 2023 Feb 27.
Article em En | MEDLINE | ID: mdl-36849465
ABSTRACT
Geological settings of the Karakoram Highway (KKH) increase the risk of natural disasters, threatening its regular operations. Predicting landslides along the KKH is challenging due to limitations in techniques, a challenging environment, and data availability issues. This study uses machine learning (ML) models and a landslide inventory to evaluate the relationship between landslide events and their causative factors. For this, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Artificial Neural Network (ANN), Naive Bayes (NB), and K Nearest Neighbor (KNN) models were used. A total of 303 landslide points were used to create an inventory, with 70% for training and 30% for testing. Susceptibility mapping used Fourteen landslide causative factors. The area under the curve (AUC) of a receiver operating characteristic (ROC) is employed to compare the accuracy of the models. The deformation of generated models in susceptible regions was evaluated using SBAS-InSAR (Small-Baseline subset-Interferometric Synthetic Aperture Radar) technique. The sensitive regions of the models showed elevated line-of-sight (LOS) deformation velocity. The XGBoost technique produces a superior Landslide Susceptibility map (LSM) for the region with the integration of SBAS-InSAR findings. This improved LSM offers predictive modeling for disaster mitigation and gives a theoretical direction for the regular management of KKH.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China