66 citations as recorded by crossref.

1. Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam H. Nguyen et al. https://doi.org/10.1002/gj.4885
2. Landslides in the Himalayas: The Role of Conditioning Factors and Their Resolution in Susceptibility Mapping L. Pathak et al. https://doi.org/10.3390/geosciences15040131
3. The Prediction of Cross-Regional Landslide Susceptibility Based on Pixel Transfer Learning X. Wang et al. https://doi.org/10.3390/rs16020347
4. Detecting information from Twitter on landslide hazards in Italy using deep learning models R. Franceschini et al. https://doi.org/10.1186/s40677-024-00279-4
5. Combining GIS and machine learning for enhanced tsunami risk management: A review of current approaches and unexplored future potential A. Tahri et al. https://doi.org/10.1051/e3sconf/202560704025
6. Towards physics-informed neural networks for landslide prediction A. Dahal & L. Lombardo https://doi.org/10.1016/j.enggeo.2024.107852
7. Global Dynamic Landslide Susceptibility Modeling Based on ResNet18: Revealing Large-Scale Landslide Hazard Evolution Trends in China H. Jiang et al. https://doi.org/10.3390/app15042038
8. Deep learning approaches for landslide information recognition: Current scenario and opportunities N. Chandra & H. Vaidya https://doi.org/10.1007/s12040-024-02281-8
9. Landslide susceptibility assessment based on an explainable ensemble model S. Heng et al. https://doi.org/10.1016/j.gr.2026.03.003
10. Explainable artificial intelligence in geoscience: A glimpse into the future of landslide susceptibility modeling A. Dahal & L. Lombardo https://doi.org/10.1016/j.cageo.2023.105364
11. Engineering geomorphology of coastal landslides at Limestone Downs, North Island, New Zealand A. Mueller et al. https://doi.org/10.1144/qjegh2024-046
12. Multivariate statistical assessment for mapping landslide susceptibility: a case study of the Yilan area, Taiwan E. Putriani et al. https://doi.org/10.1186/s40677-026-00384-6
13. High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data N. Sharma et al. https://doi.org/10.1016/j.catena.2023.107653
14. Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning analysis A. Das https://doi.org/10.1016/j.dwt.2025.101304
15. The role of conditioning factors in machine learning-based landslide spatial probability B. Nguyen & V. Doan https://doi.org/10.1680/jenge.25.00077
16. Análisis espacial de la susceptibilidad a deslizamientos con uso de geomática en la vía E20 Alóag-Santo Domingo A. Cifuentes Moya & L. Villacís Taco https://doi.org/10.33262/concienciadigital.v9i2.3621
17. Modeling windthrow effects on water runoff and hillslope stability in a mountain catchment affected by the VAIA storm L. Mauri & P. Tarolli https://doi.org/10.1016/j.scitotenv.2023.164831
18. GIS-based landslide susceptibility assessment using random forest and support vector machine models: A case study for Chin State, Myanmar S. Tun et al. https://doi.org/10.13168/AGG.2024.0019
19. Integrating GIS and ensemble learning models to predict landslide-prone zones in Chamoli District, India S. Kunwar et al. https://doi.org/10.1007/s42452-025-07694-8
20. Landslide susceptibility mapping using artificial intelligence models: a case study in the Himalayas M. Hussain et al. https://doi.org/10.1007/s10346-025-02466-2
21. Optimization of negative sample selection for landslide susceptibility mapping based on machine learning using K-means-KNN algorithm C. Liu https://doi.org/10.1007/s12145-023-01151-z
22. Landslide susceptibility assessment for Uttarakhand, a Himalayan state of India, using multi-criteria decision making, bivariate, and machine learning models V. Chauhan et al. https://doi.org/10.1186/s40677-024-00307-3
23. Evolutionary controls on post-seismic new landslides revealed by multi-earthquake inventories C. Xi et al. https://doi.org/10.1007/s10346-026-02784-z
24. An integrated approach for landslide susceptibility mapping: a case study of Idukki District, South-West India B. Athul et al. https://doi.org/10.1016/j.asr.2026.02.025
25. Geomorphological analysis and landslide susceptibility assessment of the Valcarene drainage basin (Elba Island, Northern Tyrrhenian Sea) P. Marrese et al. https://doi.org/10.1080/17445647.2026.2632459
26. Landslide susceptibility mapping using hybrid machine learning classifiers: a case study of Neelum Valley, Pakistan S. Meena et al. https://doi.org/10.1007/s10064-025-04270-7
27. Spatial Prediction of Landslide Susceptibility Using a Deep Learning and Partition Membership Hybrid Model Q. Lin & H. Hong https://doi.org/10.1111/tgis.70255
28. Decoupling urban and non-urban landslides for susceptibility mapping in transitional landscapes: a case study from Southwestern Constantine, Algeria Z. Matougui et al. https://doi.org/10.5194/nhess-25-4629-2025
29. Beyond Generalisation: Regionally Calibrated Feature Selection with Machine Learning for Data-driven Landslide Hazard Assessment in the Himalayas M. Dwivedi et al. https://doi.org/10.1007/s41748-026-01047-0
30. Quantitative forecasting of river-blocking catastrophes: decoupling the roles of geomorphic, hydrologic and seismic drivers in post-earthquake debris flow sequences M. Chen et al. https://doi.org/10.1007/s10064-026-04872-9
31. Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods E. Bravo-López et al. https://doi.org/10.3390/land12061135
32. A systematic framework for the integration of feature selection and artificial intelligence in landslide susceptibility assessment T. Kaynak https://doi.org/10.1007/s11069-025-07910-z
33. Research on Core Factor Sets for Landslide Susceptibility Mapping Based on Interpretable Machine Learning Methods X. Yu & H. Wang https://doi.org/10.3390/app16094219
34. Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility R. Ajin et al. https://doi.org/10.1038/s41598-024-72663-x
35. Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, Ecuador E. Bravo-López et al. https://doi.org/10.3390/a18050258
36. Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms L. Moualla et al. https://doi.org/10.3390/s24082637
37. 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 https://doi.org/10.1007/s12665-024-11911-9
38. GIS-based machine learning models for assessing landslide impact in King County, Washington, USA D. Lu & T. Oguchi https://doi.org/10.1186/s40645-026-00817-8
39. 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. https://doi.org/10.1007/s11356-023-31352-4
40. Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy S. Segoni et al. https://doi.org/10.3390/rs16234491
41. The suitability of different vegetation indices to analyses area with landslide propensity using Sentinel -2 Image L. Giordano et al. https://doi.org/10.1590/s1982-21702023000300008
42. Integrating interpretable artificial neural networks, geomorphic plausibility and transfer learning for landslide susceptibility mapping: a case study of Pacitan, East Java A. Mulabbi et al. https://doi.org/10.1007/s43621-025-01959-3
43. Machine-Learning-Based Hybrid Modeling for Geological Hazard Susceptibility Assessment in Wudou District, Bailong River Basin, China Z. Wang et al. https://doi.org/10.3390/geohazards4020010
44. The influence of sampling strategies on shallow landslide susceptibility modelling in a mountainous area K. Ersayin https://doi.org/10.1007/s10346-025-02646-0
45. Quantifying human risk from rainfall‐induced landslides across different return periods: A case study of Ba To, Quang Ngai, Vietnam B. Nguyen & T. Nguyen https://doi.org/10.1002/esp.70210
46. Geospatial assessment of landslide-prone areas in the southern part of Anambra State, Nigeria using classical statistical models V. Nwazelibe & J. Egbueri https://doi.org/10.1007/s12665-024-11533-1
47. Convolutional Neural Network-Based Risk Assessment of Regional Susceptibility to Road Collapse Disasters: A Case Study in Guangxi C. Li et al. https://doi.org/10.3390/app15063108
48. Optimizing Landslide Susceptibility Mapping with Non-Landslide Sampling Strategy and Spatio-Temporal Fusion Models J. Deng et al. https://doi.org/10.3390/w17121778
49. Reservoir Basin-Scale Landslide Susceptibility Assessment by Machine Learning Techniques: A Case Study of San Pietro Dam, Southern Italy E. Chikalamo et al. https://doi.org/10.3390/geosciences16040153
50. 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 https://doi.org/10.1007/s12665-022-10515-5
51. Optimization method of conditioning factors selection and combination for landslide susceptibility prediction F. Huang et al. https://doi.org/10.1016/j.jrmge.2024.04.029
52. Comparison of different machine learning models coupling with logistic regression for landslide susceptibility mapping J. Ji et al. https://doi.org/10.1016/j.gr.2025.12.014
53. Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique C. Zhou et al. https://doi.org/10.1080/10106049.2024.2327463
54. Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy X. Shao et al. https://doi.org/10.3390/app15147937
55. Assessing the effectiveness of “River Morphodynamic Corridors” for flood hazard mapping A. Brenna et al. https://doi.org/10.1016/j.geomorph.2024.109460
56. Landslide susceptibility of Rwanda (Central Africa) C. Panelli et al. https://doi.org/10.1080/17445647.2024.2428654
57. Integrating AI and climate change scenarios for multi-risk assessment in the coastal municipalities of the Veneto region M. Dal Barco et al. https://doi.org/10.1016/j.scitotenv.2025.178586
58. Application of deep learning, machine learning and multi-criteria decision analysis for ecotourism potentiality assessment: a case study of the Sundarban Biosphere Reserve, India A. Baidya et al. https://doi.org/10.1007/s44163-025-00496-2
59. 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. https://doi.org/10.1007/s12665-024-11838-1
60. Towards automatic delineation of landslide source and runout K. Bhuyan et al. https://doi.org/10.1016/j.enggeo.2024.107866
61. Integrating Eco-DRR into landslide susceptibility assessment: The critical role of eco-environmental factors M. Broquet et al. https://doi.org/10.1016/j.jenvman.2025.127043
62. Landslide susceptibility mapping of Phewa Watershed, Kaski, Nepal B. Kunwar et al. https://doi.org/10.21595/mme.2024.24231
63. Integrating GIS and comparative multi-criteria decision-making techniques for landslide susceptibility assessment R. Faisal & Z. Shehab https://doi.org/10.1177/03091333251380439
64. Generating multi-temporal landslide inventories through a general deep transfer learning strategy using HR EO data K. Bhuyan et al. https://doi.org/10.1038/s41598-022-27352-y
65. Machine Learning-Driven Landslide Susceptibility Mapping in the Himalayan China–Pakistan Economic Corridor Region M. Ullah et al. https://doi.org/10.3390/land13071011
66. Landslide Susceptibility Analysis on the Vicinity of Bogotá-Villavicencio Road (Eastern Cordillera of the Colombian Andes) M. Herrera-Coy et al. https://doi.org/10.3390/rs15153870