34 citations as recorded by crossref.

1. Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility R. Ajin et al. 10.1038/s41598-024-72663-x
2. Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam H. Nguyen et al. 10.1002/gj.4885
3. Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms L. Moualla et al. 10.3390/s24082637
4. The Prediction of Cross-Regional Landslide Susceptibility Based on Pixel Transfer Learning X. Wang et al. 10.3390/rs16020347
5. Detecting information from Twitter on landslide hazards in Italy using deep learning models R. Franceschini et al. 10.1186/s40677-024-00279-4
6. Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential A. Tahri et al. 10.1051/e3sconf/202560704025
7. Application of ML- based approach for co-seismic landslides susceptibility mapping and identification of important controlling factors in eastern Himalayan region S. Kumar & A. Sengupta 10.1007/s12665-024-11911-9
8. Towards physics-informed neural networks for landslide prediction A. Dahal & L. Lombardo 10.1016/j.enggeo.2024.107852
9. Deep learning approaches for landslide information recognition: Current scenario and opportunities N. Chandra & H. Vaidya 10.1007/s12040-024-02281-8
10. Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling A. Dahal & L. Lombardo 10.1016/j.cageo.2023.105364
11. Evaluating the influence of road construction on landslide susceptibility in Saudi Arabia’s mountainous terrain: a Bayesian-optimised deep learning approach with attention mechanism and sensitivity analysis S. Alqadhi et al. 10.1007/s11356-023-31352-4
12. Engineering geomorphology of coastal landslides at Limestone Downs, North Island, New Zealand A. Mueller et al. 10.1144/qjegh2024-046
13. High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data N. Sharma et al. 10.1016/j.catena.2023.107653
14. Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy S. Segoni et al. 10.3390/rs16234491
15. The suitability of different vegetation indices to analyses area with landslide propensity using Sentinel -2 Image L. Giordano et al. 10.1590/s1982-21702023000300008
16. Modeling windthrow effects on water runoff and hillslope stability in a mountain catchment affected by the VAIA storm L. Mauri & P. Tarolli 10.1016/j.scitotenv.2023.164831
17. Machine-Learning-Based Hybrid Modeling for Geological Hazard Susceptibility Assessment in Wudou District, Bailong River Basin, China Z. Wang et al. 10.3390/geohazards4020010
18. GIS-based landslide susceptibility assessment using random forest and support vector machine models: A case study for Chin State, Myanmar S. Tun 10.13168/AGG.2024.0019
19. Geospatial assessment of landslide-prone areas in the southern part of Anambra State, Nigeria using classical statistical models V. Nwazelibe & J. Egbueri 10.1007/s12665-024-11533-1
20. Optimization of negative sample selection for landslide susceptibility mapping based on machine learning using K-means-KNN algorithm C. Liu 10.1007/s12145-023-01151-z
21. Landslide susceptibility assessment for Uttarakhand, a Himalayan state of India, using multi-criteria decision making, bivariate, and machine learning models V. Chauhan et al. 10.1186/s40677-024-00307-3
22. Mitigation measures preventing floods from landslide dams: analysis of pre- and post-hydrologic conditions upstream a seismic-induced landslide dam in Central Italy C. Cencetti & L. Di Matteo 10.1007/s12665-022-10515-5
23. Optimization method of conditioning factors selection and combination for landslide susceptibility prediction F. Huang et al. 10.1016/j.jrmge.2024.04.029
24. Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique C. Zhou et al. 10.1080/10106049.2024.2327463
25. Assessing the effectiveness of “River Morphodynamic Corridors” for flood hazard mapping A. Brenna et al. 10.1016/j.geomorph.2024.109460
26. Landslide susceptibility of Rwanda (Central Africa) C. Panelli et al. 10.1080/17445647.2024.2428654
27. Integrating AI and climate change scenarios for multi-risk assessment in the coastal municipalities of the Veneto region M. Dal Barco et al. 10.1016/j.scitotenv.2025.178586
28. Using multispectral images and field inclinometer data to analyze topographic changes related to and the reactivation mechanism of a large-scale landslide at Caoling in Taiwan H. Chen et al. 10.1007/s12665-024-11838-1
29. Towards automatic delineation of landslide source and runout K. Bhuyan et al. 10.1016/j.enggeo.2024.107866
30. Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods E. Bravo-López et al. 10.3390/land12061135
31. Landslide susceptibility mapping of Phewa Watershed, Kaski, Nepal B. Kunwar et al. 10.21595/mme.2024.24231
32. Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data K. Bhuyan et al. 10.1038/s41598-022-27352-y
33. Machine Learning-Driven Landslide Susceptibility Mapping in the Himalayan China–Pakistan Economic Corridor Region M. Ullah et al. 10.3390/land13071011
34. Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes) M. Herrera-Coy et al. 10.3390/rs15153870