ANALYSIS OF LANDSLIDE REACTIVATION USING SATELLITE DATA: A CASE STUDY OF KOTRUPI LANDSLIDE, MANDI, HIMACHAL PRADESH, INDIA

21 citations as recorded by crossref.

1. Landslide susceptibility assessment along highways (SH-12 and NH-717A) in Darjeeling Himalayas B. Mandal et al. https://doi.org/10.1016/j.asr.2024.10.016
2. “Ensembled transfer learning approach for error reduction in landslide susceptibility mapping of the data scare region” A. Singh et al. https://doi.org/10.1038/s41598-024-76541-4
3. LogISEL: an interpretable ensemble framework for real-time landslide prediction in the Indian Himalayas M. Chand et al. https://doi.org/10.1186/s40562-026-00477-y
4. Strategies for sampling pseudo-absences of landslide locations for landslide susceptibility mapping in complex mountainous terrain of Northwest Himalaya A. Singh et al. https://doi.org/10.1007/s10064-023-03333-x
5. Physical modelling to map the rheological and morphological dynamics along with entrainment mechanics of debris flow: a comprehensive review of the state of the art M. Bilal & R. Kumar https://doi.org/10.1186/s40677-025-00349-1
6. Landslide-induced debris flows and its investigation using r.avaflow: A case study from Kotrupi, India N. Pandey et al. https://doi.org/10.1007/s12040-024-02315-1
7. First- and Second-Order Reliability Analysis of Rainfall-Induced Kotropi Slope Failure B. Basha & A. Raghuram https://doi.org/10.1007/s40098-022-00700-5
8. ML-CASCADE: A machine learning and cloud computing-based tool for rapid and automated mapping of landslides using earth observation data N. Sharma & M. Saharia https://doi.org/10.1007/s10346-024-02360-3
9. Chronicle of destruction: the Wayanad landslide of July 30, 2024 A. Yunus et al. https://doi.org/10.1007/s10346-025-02494-y
10. Effect of groundwater table fluctuation on slope instability: a comprehensive 3D simulation approach for Kotropi landslide, India S. Panda et al. https://doi.org/10.1007/s10346-022-01993-6
11. Study of rainfall-induced landslide using a self-developed tilt monitoring system: a physical and numerical modelling approach A. Paswan & A. Shrivastava https://doi.org/10.1127/zdgg/2023/0399
12. Synergetic use of geospatial and machine learning techniques in modelling landslide susceptibility in parts of Shimla to Kinnaur National Highway, Himachal Pradesh R. Das et al. https://doi.org/10.1007/s40808-024-01993-6
13. Reactivated Wuwanchai landslide area monitoring and analysis C. Chen et al. https://doi.org/10.1007/s10064-022-02905-7
14. Artificial Intelligence Techniques for Landslides Prediction Using Satellite Imagery A. Sharma et al. https://doi.org/10.1109/ACCESS.2024.3446037
15. Estimating the period of probable landslide event using advanced D-InSAR technique for time-series deformation study of Kotrupi region K. Niraj et al. https://doi.org/10.1080/19475705.2023.2281245
16. Assessment of an amended soil as a climate adaptive barrier: Element testing and physical modelling A. Rana et al. https://doi.org/10.1016/j.gete.2025.100693
17. Identification of potential failure zones in large progressive landslides: a method to optimize the cost of landslide early warning S. Kumar et al. https://doi.org/10.1007/s11069-024-06685-z
18. Kotrupi landslide deformation study in non-urban area using DInSAR and MTInSAR techniques on Sentinel-1 SAR data K. Niraj et al. https://doi.org/10.1016/j.asr.2021.11.042
19. Numerical modeling of high-magnitude mass movement in data-scarce Himalayan terrain: a study using Lagrangian based modified DualSPHysics tool N. Pandey & N. Satyam https://doi.org/10.1007/s11069-025-07959-w
20. A new random forest method for landslide susceptibility mapping using hyperparameter optimization and grid search techniques M. Kanwar et al. https://doi.org/10.1007/s13762-024-06310-3
21. Evaluation of a Tilt-Based Monitoring System for Rainfall-Induced Landslides: Development and Physical Modelling A. Paswan & A. Shrivastava https://doi.org/10.3390/w15101862