68 citations as recorded by crossref.

1. Application of GIS-based data-driven bivariate statistical models for landslide prediction: a case study of highly affected landslide prone areas of Teesta River basin I. Poddar & R. Roy https://doi.org/10.1016/j.qsa.2023.100150
2. Topographic profile and morphology analysis of shallow landslides inside and outside of forests with a semi-automatic mapping approach and bi-temporal airborne laser scanning data L. de Vugt et al. https://doi.org/10.5194/nhess-26-1375-2026
3. Detailed Inventory and Spatial Distribution Analysis of Rainfall-Induced Landslides in Jiexi County, Guangdong Province, China in August 2018 C. Xie et al. https://doi.org/10.3390/su151813930
4. Insight into the Characteristics and Triggers of Loess Landslides during the 2013 Heavy Rainfall Event in the Tianshui Area, China X. Shao et al. https://doi.org/10.3390/rs15174304
5. Assessing Consistency and Inconsistency in Landslide Susceptibility Mapping: Quality Criteria and Pixel-Level Analysis in a Case Study from the Alborz Mountains, North of Tehran, Iran T. Kreuzer et al. https://doi.org/10.1007/s11069-025-07487-7
6. Using Sentinel-1 radar amplitude time series to constrain the timings of individual landslides: a step towards understanding the controls on monsoon-triggered landsliding K. Burrows et al. https://doi.org/10.5194/nhess-22-2637-2022
7. Large landslides in the upper Jinsha River basin: comprehensive inventory and implications L. Li et al. https://doi.org/10.1080/10095020.2025.2582301
8. How to better link landslide inventory mapping with loss and damage reporting D. Urueña et al. https://doi.org/10.1016/j.ijdrr.2026.106242
9. Understanding fatal landslides at global scales: a summary of topographic, climatic, and anthropogenic perspectives S. Fidan et al. https://doi.org/10.1007/s11069-024-06487-3
10. Landslide susceptibility classification using multi hive artificial bee colony programming: A novel symbolic regression framework S. Arslan et al. https://doi.org/10.1016/j.eswa.2025.129324
11. A novel landslide susceptibility prediction framework based on contrastive loss S. Ouyang et al. https://doi.org/10.1080/15481603.2024.2306740
12. Rainfall-seismic coupling effect induced landslide hazard assessment Z. Li et al. https://doi.org/10.1007/s11069-023-06084-w
13. Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China Z. Wang et al. https://doi.org/10.3390/ijerph19159412
14. 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
15. Proterozoic landform characterization for land use sustainability using automated geomorphon and topographic position index in the Eastern Chhotanagpur plateau fringe, India D. Mandal & D. Ghosh https://doi.org/10.1007/s43538-025-00525-9
16. Size scaling of large landslides from incomplete inventories O. Korup et al. https://doi.org/10.5194/nhess-24-3815-2024
17. Severe rainfall-induced landslides in Pingyuan County, Guangdong, China, in June 2024 W. Zhang et al. https://doi.org/10.1007/s10346-025-02546-3
18. Decoding dynamic landslide hazard processes for a massive refugee camp in Bangladesh D. Haque et al. https://doi.org/10.1016/j.envc.2025.101172
19. Establishing a Landslide Traces Inventory for the Baota District, Yan’an City, China, Using High-Resolution Satellite Images S. Zhang et al. https://doi.org/10.3390/land13101580
20. Toward Knowledge-Enhanced Geohazard Intelligence: A Review of Knowledge Graphs and Large Language Models W. Li & Y. Zhou https://doi.org/10.3390/geohazards7020040
21. Modelling antecedent soil hydrological conditions to improve the prediction of landslide susceptibility in typhoon-prone regions C. Abancó et al. https://doi.org/10.1007/s10346-024-02242-8
22. Important considerations in machine learning-based landslide susceptibility assessment under future climate conditions Y. Han & S. Semnani https://doi.org/10.1007/s11440-024-02363-3
23. Destabilization Mechanism of Rainfall-Induced Loess Landslides in the Kara Haisu Gully, Xinyuan County, Ili River Valley, China: Physical Simulation T. Zhang et al. https://doi.org/10.3390/w15213775
24. From slopes to streams: Geospatial characterization of landslide dynamics and downstream impacts in the Western Ghats highland panchayats, Kerala, India A. Amrutha et al. https://doi.org/10.64866/j.ijdscr.2026.10025
25. Near Pan-Svalbard permafrost cryospheric hazards inventory (SvalCryo) I. Nicu et al. https://doi.org/10.1038/s41597-024-03754-7
26. Event-based rainfall windows and topographic controls on landslide susceptibility in West Sumatra: A machine-learning analysis A. Octova et al. https://doi.org/10.15243/jdmlm.2026.132.9759
27. Three-dimensional stability analysis and groundwater table estimation of a retrogressive shallow soil landslide: A case study of the Zhongzhai landslide in Gansu Province, China S. Jia et al. https://doi.org/10.1007/s10064-025-04160-y
28. Improving the spatial prediction of machine learning-based landslide susceptibility models by integrating the particle swarm optimization algorithm R. Ajin et al. https://doi.org/10.1007/s00477-025-03101-1
29. Capturing the complete landslide–debris-rich flood continuum for accurate inventory, susceptibility and exposure mapping – lessons from Cyclone Idai A. Dille et al. https://doi.org/10.5194/nhess-26-2561-2026
30. Multi-event assessment of typhoon-triggered landslide susceptibility in the Philippines J. Jones et al. https://doi.org/10.5194/nhess-23-1095-2023
31. Landslide Traces Inventory and Spatial Distribution Analysis Along the Hubei Section of the Jinsha River–Hubei Ultra-High-Voltage Transmission Line, China W. Yang et al. https://doi.org/10.3390/f16111686
32. Dual control of bedding structure on catchment hydrology: the interplay between direct bedding-parallel recharge and indirect landslide-driven drainage in steep accretionary complexes Y. Yamakawa et al. https://doi.org/10.1016/j.catena.2026.110236
33. Assessment of Potential Landslide Scenarios Using Morphometry, Geomorphological Constraints, and Run-Out Analysis: A Case Study from Central Apennines (Italy) G. Paglia et al. https://doi.org/10.3390/land14112109
34. SUGARFuseNet: Diffusion‑driven domain adaptation and bimodal bitemporal fusion for advancing global landslide segmentation on novel GBMT‑SLID dataset G. Emani et al. https://doi.org/10.1016/j.neucom.2026.134060
35. Time series analysis of slope displacements using UAV photogrammetry and its relationship with rainfall intensity N. Kim et al. https://doi.org/10.1007/s10346-024-02249-1
36. Timing landslide and flash flood events from SAR satellite: a regionally applicable methodology illustrated in African cloud-covered tropical environments A. Deijns et al. https://doi.org/10.5194/nhess-22-3679-2022
37. Multi-hazard susceptibility mapping of cryospheric hazards in a high-Arctic environment: Svalbard Archipelago I. Nicu et al. https://doi.org/10.5194/essd-15-447-2023
38. Overcoming the data limitations in landslide susceptibility modeling J. Woodard & B. Mirus https://doi.org/10.1126/sciadv.adt1541
39. Rapid Mapping of Rainfall-Induced Landslide Using Multi-Temporal Satellite Data M. Aman et al. https://doi.org/10.3390/rs17081407
40. Exploiting earthquake-induced landslide inventories for macroseismic assessment using the environmental seismic intensity (ESI-07) scale E. Muccignato & M. Ferrario https://doi.org/10.3389/feart.2025.1468787
41. The influence of spatial patterns in rainfall on shallow landslides H. Smith et al. https://doi.org/10.1016/j.geomorph.2023.108795
42. Demystifying the predictive capability of advanced heterogeneous machine learning ensembles for landslide susceptibility assessment and mapping in the Eastern Himalayan Region, India S. Dey et al. https://doi.org/10.1007/s11069-025-07325-w
43. Deep learning models for comprehensive landslide susceptibility study considering climate change and sustainable development implications in Western Province of Rwanda V. Nwazelibe et al. https://doi.org/10.1007/s11069-026-08182-x
44. The Evaluation of Rainfall Warning Thresholds for Shallow Slope Stability Based on the Local Safety Factor Theory Y. Yang et al. https://doi.org/10.3390/geosciences14100274
45. Landslides Triggered by the 2016 Heavy Rainfall Event in Sanming, Fujian Province: Distribution Pattern Analysis and Spatio-Temporal Susceptibility Assessment S. Ma et al. https://doi.org/10.3390/rs15112738
46. A Spatio-Temporal Dataset for Satellite-Based Landslide Detection P. Höhn et al. https://doi.org/10.1038/s41597-025-06167-2
47. Global Assessment of the Capability of Satellite Precipitation Products to Retrieve Landslide-Triggering Extreme Rainfall Events O. Marc et al. https://doi.org/10.1175/EI-D-21-0022.1
48. Coupling physical and social resilience in disaster risk governance: Insights from landslide-prone mountain regions of the eastern Tibetan plateau M. Li et al. https://doi.org/10.1016/j.eiar.2026.108351
49. Hydrogeotechnical Predictive Approach for Rockfall Mountain Hazard Using Elastic Modulus and Peak Shear Stress at Soil–Rock Interface in Dry and Wet Phases at KKH Pakistan E. Mehmood et al. https://doi.org/10.3390/su142416740
50. An open event-inventory database of rainfall-induced landslides and their environmental characteristics in the eastern Black Sea region of Türkiye R. Çömert et al. https://doi.org/10.1016/j.enggeo.2025.108258
51. Preliminary analysis of the July 30, 2024, Wayanad landslide disaster in India: Causes and impacts Q. Wang et al. https://doi.org/10.1016/j.nhres.2025.04.005
52. Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy S. Segoni et al. https://doi.org/10.3390/rs16234491
53. A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities W. Han et al. https://doi.org/10.1016/j.isprsjprs.2023.05.032
54. Rapid Mapping of Landslides Induced by Heavy Rainfall in the Emilia-Romagna (Italy) Region in May 2023 M. Ferrario & F. Livio https://doi.org/10.3390/rs16010122
55. Is a climate signal detectable using cosmogenic data and coarse‐resolution digital topography in fluvially dominated landscapes? C. Xu et al. https://doi.org/10.1002/esp.70164
56. Short to long term space-time prediction of rain-induced landslides under uncertainty A. Mondini et al. https://doi.org/10.1016/j.scitotenv.2025.179453
57. GIS-based statistical analysis of rainfall-induced landslides: A case study of the 2018 Kerala event N. Rai et al. https://doi.org/10.1007/s12040-025-02639-6
58. Estimating global landslide susceptibility and its uncertainty through ensemble modeling A. Felsberg et al. https://doi.org/10.5194/nhess-22-3063-2022
59. Spatial footprints of moisture-driven landslides in Western Himalayas from 2007 to 2022 K. Kumari et al. https://doi.org/10.1007/s11069-024-07086-y
60. Research on a dynamic early warning model based on refined threshold analysis: Case study of the Tanjiawan Landslide W. Wang et al. https://doi.org/10.1371/journal.pone.0339689
61. Spatial and Temporal Analysis of Global Landslide Reporting Using a Decade of the Global Landslide Catalog C. Dandridge et al. https://doi.org/10.3390/su15043323
62. Conventional and advanced geospatial techniques for landslide detection and modeling: a comprehensive overview M. Shafapourtehrany et al. https://doi.org/10.1186/s40677-025-00347-3
63. Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China S. Ma et al. https://doi.org/10.3390/rs14174257
64. A hybrid deep learning method for landslide susceptibility analysis with the application of InSAR data R. Yuan & J. Chen https://doi.org/10.1007/s11069-022-05430-8
65. Revealing the relation between spatial patterns of rainfall return levels and landslide density S. Mtibaa & H. Tsunetaka https://doi.org/10.5194/esurf-11-461-2023
66. The source-sink effects of plain and plateau urban morphology on land surface temperature across seasonal and diurnal cycles: A case study of Lhasa and Wuhan R. Liu et al. https://doi.org/10.1016/j.enbuild.2026.117451
67. A machine-learning-based multi-hazard GIS-AHP framework for wind turbine siting under earthquake–landslide coupling Ö. Dilmen et al. https://doi.org/10.1088/2515-7620/ae69a0
68. Assessing shallow slope stability using electrical conductivity data and soil hydraulic characteristics Y. Yang et al. https://doi.org/10.1016/j.enggeo.2024.107447