64 citations as recorded by crossref.

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5. National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making M. Moharrami et al. https://doi.org/10.3390/ijgi9060393
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7. A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests Z. Shirvani https://doi.org/10.3390/rs12030434
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11. Landslide susceptibility mapping and management in Western Serbia: an analysis of ANFIS- and SVM-based hybrid models I. Elkhrachy et al. https://doi.org/10.3389/fenvs.2023.1218954
12. Deep learning algorithms based landslide vulnerability modeling in highly landslide prone areas of Tamil Nadu, India S. Saha et al. https://doi.org/10.1007/s12303-024-0044-y
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14. General Department of Natural Resources and Watershed Management, Mazandaran, Sari, Iran M. Nemati Koutenaee https://doi.org/10.61186/jert.45050.9.15.45
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17. Real-time early warning of landslide disaster risks on major highways in Ganzhou City, China F. Huang et al. https://doi.org/10.1016/j.jrmge.2025.10.023
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19. Analysis of land use and coverage and its relationships with slope instability events on the Carajás Railway: The case of the Pindaré River Basin in the Brazilian Amazon biome M. Rodrigues de Sousa et al. https://doi.org/10.1016/j.jsames.2025.105373
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21. Dynamics of past forest cover changes and future scenarios with implications for soil degradation in Misiones rainforest, Argentina M. Navarro Rau et al. https://doi.org/10.1016/j.jnc.2023.126391
22. Evaluation of ground conditions and vehicle-induced vibration using Spatio-geophysical approach for the Nilgiris landslide transportation interaction corridor, Western Ghats, India S. Chellamuthu & G. Ganapathy https://doi.org/10.1080/19475705.2025.2532046
23. Assessing earthquake-induced landslide susceptibility: a comparative study with and without landslide inventory data in the Indian Himalayan region . Sangeeta et al. https://doi.org/10.1007/s11069-026-08168-9
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25. Landslide dynamic susceptibility mapping in urban expansion area considering spatiotemporal land use and land cover change F. Zhao et al. https://doi.org/10.1016/j.scitotenv.2024.175059
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27. Land use and land cover as a conditioning factor in landslide susceptibility: a literature review R. Pacheco Quevedo et al. https://doi.org/10.1007/s10346-022-02020-4
28. Landslide Susceptibility Mapping Based on Multitemporal Remote Sensing Image Change Detection and Multiexponential Band Math X. Yu et al. https://doi.org/10.3390/su15032226
29. Flood risk assessment of the European road network K. van Ginkel et al. https://doi.org/10.5194/nhess-21-1011-2021
30. Implications of future land-use/cover pattern change on landslide susceptibility at a national level: A scenario-based analysis in Romania M. Jurchescu et al. https://doi.org/10.1016/j.catena.2023.107330
31. GIS-based landslide susceptibility mapping using ensemble methods for Fengjie County in the Three Gorges Reservoir Region, China W. Gong et al. https://doi.org/10.1007/s13762-021-03572-z
32. Impacts of future climate and land cover changes on landslide susceptibility: regional scale modelling in the Val d’Aran region (Pyrenees, Spain) M. Hürlimann et al. https://doi.org/10.1007/s10346-021-01775-6
33. Beyond boundaries: AI-optimized global landslide susceptibility mapping M. Panahi et al. https://doi.org/10.1080/19475705.2025.2493222
34. Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models S. Mahato et al. https://doi.org/10.1016/j.gsf.2021.101175
35. 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
36. Assessing the stability conditions of a slope movement in Northern Italy interacting with the provincial road network A. Valletta et al. https://doi.org/10.1016/j.trpro.2023.11.243
37. INVESTIGATING THE IMPACT OF PAN SHARPENING ON THE ACCURACY OF LAND COVER MAPPING IN LANDSAT OLI IMAGERY K. Rokni https://doi.org/10.3846/gac.2023.15308
38. Assessing regressive erosion effects: Unveiling riverside land use land cover changes post hydroelectric project construction A. Velastegui-Montoya et al. https://doi.org/10.1016/j.envc.2024.100882
39. Exploring the Role of Land Use Settings on Landslide Ellipticity in Volcanic Foot Slope of the Southern Sumbing Mountain, Central-Java Indonesia R. Purwaningsih et al. https://doi.org/10.24057/2071-9388-2026-3962
40. Analysis of landslide explicative factors and susceptibility mapping in an andean context: The case of Azuay province (Ecuador) S. Cobos-Mora et al. https://doi.org/10.1016/j.heliyon.2023.e20170
41. Landslide susceptibility prediction considering land use change and human activity: A case study under rapid urban expansion and afforestation in China H. Xiong et al. https://doi.org/10.1016/j.scitotenv.2023.161430
42. Improving pixel-based regional landslide susceptibility mapping X. Wei et al. https://doi.org/10.1016/j.gsf.2024.101782
43. Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia F. Li et al. https://doi.org/10.3390/app11093768
44. Spatiotemporal changes of landslide susceptibility in response to rainfall and its future prediction — A case study of Sichuan Province, China H. Zheng & M. Ding https://doi.org/10.1016/j.ecoinf.2024.102862
45. Prediction of the future landslide susceptibility scenario based on LULC and climate projections A. Tyagi et al. https://doi.org/10.1007/s10346-023-02088-6
46. Application of novel deep boosting framework-based earthquake induced landslide hazards prediction approach in Sikkim Himalaya I. Chowdhuri et al. https://doi.org/10.1080/10106049.2022.2068675
47. Landslide Susceptibility Using Climatic–Environmental Factors Using the Weight-of-Evidence Method—A Study Area in Central Italy M. Gentilucci et al. https://doi.org/10.3390/app13158617
48. Probabilistic connectivity assessment of road networks exposed to spatially correlated rainfall-triggered landslides Z. He et al. https://doi.org/10.1016/j.ress.2025.110800
49. Multi-Scale Attention Network for Landslide Susceptibility Assessment Z. Zhan et al. https://doi.org/10.3390/geosciences16050188
50. Landslide spatial modelling using unsupervised factor optimisation and regularised greedy forests M. Sameen et al. https://doi.org/10.1016/j.cageo.2019.104336
51. 4D electrical resistivity tomography for assessing the influence of vegetation and subsurface moisture on railway cutting condition J. Holmes et al. https://doi.org/10.1016/j.enggeo.2022.106790
52. Structural Changes in the Romanian Economy Reflected through Corine Land Cover Datasets A. Rusu et al. https://doi.org/10.3390/rs12081323
53. Landslide risk of regional roads: Consider the road mileage of expected losses Q. Liu et al. https://doi.org/10.1016/j.trd.2023.103771
54. Use of delayed ERA5-Land soil moisture products for improving landslide early warning N. Palazzolo et al. https://doi.org/10.5194/nhess-25-4907-2025
55. Assessing and mapping landslide susceptibility using different machine learning methods O. Orhan et al. https://doi.org/10.1080/10106049.2020.1837258
56. Mapping the landslide susceptibility considering future land-use land-cover scenario A. Tyagi et al. https://doi.org/10.1007/s10346-022-01968-7
57. Impact of Land Use/Land Cover Change on Landslide Susceptibility in Rangamati Municipality of Rangamati District, Bangladesh Y. Rabby et al. https://doi.org/10.3390/ijgi11020089
58. Land-Cover Semantic Segmentation for Very-High-Resolution Remote Sensing Imagery Using Deep Transfer Learning and Active Contour Loss M. Chicchon et al. https://doi.org/10.1109/ACCESS.2025.3556632
59. Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India U. Sur et al. https://doi.org/10.1007/s10668-021-01226-1
60. Landslide susceptibility zonation (LSZ) in parts of South Andaman Island, India using Multi-Criteria Analysis S. Maniruzzaman et al. https://doi.org/10.1007/s43538-025-00601-0
61. Landslide-related maintenance issues around mountain road in Dasha River section of Central Cross Island Highway, Taiwan M. Tsao et al. https://doi.org/10.1007/s10064-020-01967-9
62. GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms S. Ali et al. https://doi.org/10.1016/j.gsf.2020.09.004
63. Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China H. Shang et al. https://doi.org/10.3390/rs15204952
64. Terrain visibility impact on the preparation of landslide inventories: a practical example in Darjeeling district (India) T. Bornaetxea et al. https://doi.org/10.5194/nhess-22-2929-2022