These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
246 related articles for article (PubMed ID: 31207868)
1. Landslide Susceptibility Mapping of Karakorum Highway Combined with the Application of SBAS-InSAR Technology. Zhao F; Meng X; Zhang Y; Chen G; Su X; Yue D Sensors (Basel); 2019 Jun; 19(12):. PubMed ID: 31207868 [TBL] [Abstract][Full Text] [Related]
2. SBAS-InSAR based validated landslide susceptibility mapping along the Karakoram Highway: a case study of Gilgit-Baltistan, Pakistan. Kulsoom I; Hua W; Hussain S; Chen Q; Khan G; Shihao D Sci Rep; 2023 Feb; 13(1):3344. PubMed ID: 36849465 [TBL] [Abstract][Full Text] [Related]
3. Landslide Susceptibility Mapping with Integrated SBAS-InSAR Technique: A Case Study of Dongchuan District, Yunnan (China). Zhu Z; Gan S; Yuan X; Zhang J Sensors (Basel); 2022 Jul; 22(15):. PubMed ID: 35898090 [TBL] [Abstract][Full Text] [Related]
4. Landslide Susceptibility Mapping Using Machine Learning Algorithm Validated by Persistent Scatterer In-SAR Technique. Hussain MA; Chen Z; Zheng Y; Shoaib M; Shah SU; Ali N; Afzal Z Sensors (Basel); 2022 Apr; 22(9):. PubMed ID: 35590807 [TBL] [Abstract][Full Text] [Related]
5. Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China. Xiao B; Zhao J; Li D; Zhao Z; Zhou D; Xi W; Li Y Sensors (Basel); 2022 Oct; 22(20):. PubMed ID: 36298394 [TBL] [Abstract][Full Text] [Related]
6. Identifying the essential influencing factors of landslide susceptibility models based on hybrid-optimized machine learning with different grid resolutions: a case of Sino-Pakistani Karakorum Highway. Wu J; Zhang Y; Yang L; Zhang Y; Lei J; Zhi M; Ma G Environ Sci Pollut Res Int; 2023 Sep; 30(45):100675-100700. PubMed ID: 37639095 [TBL] [Abstract][Full Text] [Related]
7. Comparison of Random Forest Model and Frequency Ratio Model for Landslide Susceptibility Mapping (LSM) in Yunyang County (Chongqing, China). Wang Y; Sun D; Wen H; Zhang H; Zhang F Int J Environ Res Public Health; 2020 Jun; 17(12):. PubMed ID: 32545618 [TBL] [Abstract][Full Text] [Related]
8. Detection and analysis of potential landslides based on SBAS-InSAR technology in alpine canyon region. Li Y; Feng X; Li Y; Jiang W; Yu W Environ Sci Pollut Res Int; 2024 Jan; 31(4):6492-6510. PubMed ID: 38151559 [TBL] [Abstract][Full Text] [Related]
9. Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan. Hussain S; Pan B; Afzal Z; Ali M; Zhang X; Shi X; Ali M Sci Rep; 2023 May; 13(1):7485. PubMed ID: 37161025 [TBL] [Abstract][Full Text] [Related]
10. Dynamic landslide susceptibility mapping based on the PS-InSAR deformation intensity. Jin B; Zeng T; Yin K; Gui L; Guo Z; Wang T Environ Sci Pollut Res Int; 2024 Jan; 31(5):7872-7888. PubMed ID: 38170358 [TBL] [Abstract][Full Text] [Related]
11. Identification of potential landslide in Jianzha county based on InSAR and deep learning. Yang X; Chen D; Dong Y; Xue Y; Qin K Sci Rep; 2024 Sep; 14(1):21346. PubMed ID: 39285244 [TBL] [Abstract][Full Text] [Related]
12. Landslide Monitoring along the Dadu River in Sichuan Based on Sentinel-1 Multi-Temporal InSAR. Huang H; Ju S; Duan W; Jiang D; Gao Z; Liu H Sensors (Basel); 2023 Mar; 23(7):. PubMed ID: 37050447 [TBL] [Abstract][Full Text] [Related]
13. Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change. Zhao F; Miao F; Wu Y; Gong S; Zheng G; Yang J; Zhan W Sci Total Environ; 2024 Nov; 949():175059. PubMed ID: 39084358 [TBL] [Abstract][Full Text] [Related]
14. Research on Time Series Monitoring of Surface Deformation in Tongliao Urban Area Based on SBAS-PS-DS-InSAR. Chen Y; Ding C; Huang P; Yin B; Tan W; Qi Y; Xu W; Du S Sensors (Basel); 2024 Feb; 24(4):. PubMed ID: 38400328 [TBL] [Abstract][Full Text] [Related]
15. Monitoring and analysis of ground subsidence in Shanghai based on PS-InSAR and SBAS-InSAR technologies. Zhang Z; Hu C; Wu Z; Zhang Z; Yang S; Yang W Sci Rep; 2023 May; 13(1):8031. PubMed ID: 37198287 [TBL] [Abstract][Full Text] [Related]
16. Monitoring Land Subsidence in Wuhan City (China) using the SBAS-InSAR Method with Radarsat-2 Imagery Data. Zhang Y; Liu Y; Jin M; Jing Y; Liu Y; Liu Y; Sun W; Wei J; Chen Y Sensors (Basel); 2019 Feb; 19(3):. PubMed ID: 30759841 [TBL] [Abstract][Full Text] [Related]
17. Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Dou J; Yunus AP; Tien Bui D; Merghadi A; Sahana M; Zhu Z; Chen CW; Khosravi K; Yang Y; Pham BT Sci Total Environ; 2019 Apr; 662():332-346. PubMed ID: 30690368 [TBL] [Abstract][Full Text] [Related]
18. Comparing probabilistic and statistical methods in landslide susceptibility modeling in Rwanda/Centre-Eastern Africa. Nsengiyumva JB; Luo G; Amanambu AC; Mind'je R; Habiyaremye G; Karamage F; Ochege FU; Mupenzi C Sci Total Environ; 2019 Apr; 659():1457-1472. PubMed ID: 31096356 [TBL] [Abstract][Full Text] [Related]
19. Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China. Wang Y; Wu X; Chen Z; Ren F; Feng L; Du Q Int J Environ Res Public Health; 2019 Jan; 16(3):. PubMed ID: 30696105 [TBL] [Abstract][Full Text] [Related]
20. Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology. Saha A; Tripathi L; Villuri VGK; Bhardwaj A Environ Sci Pollut Res Int; 2024 Feb; 31(7):10443-10459. PubMed ID: 38198087 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]