203 citations as recorded by crossref.

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11. Investigating causal factors of shallow landslides in grassland regions of Switzerland L. Zweifel et al. https://doi.org/10.5194/nhess-21-3421-2021
12. Future Forest Cover Change Scenarios with Implications for Landslide Risk: An Example from Buzau Subcarpathians, Romania Ž. Malek et al. https://doi.org/10.1007/s00267-015-0577-y
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18. Geospatial AI for flood susceptibility mapping: a systematic literature review of parameters, methods, uncertainty quantification, and resilience frameworks D. Sen & S. Das https://doi.org/10.3389/fenvs.2026.1896243
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20. Multi‑feature geological hazard susceptibility assessment by integrating improved ResNet and transfer learning: A case study of the Loess Plateau in Northern Shaanxi H. Cheng et al. https://doi.org/10.1016/j.rineng.2025.105946
21. Analyzing the posterior predictive capability and usability of landslide susceptibility maps: a case of Kerala, India T. Pareek et al. https://doi.org/10.1007/s10346-024-02389-4
22. Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping T. Gudiyangada Nachappa et al. https://doi.org/10.1080/19475705.2020.1736190
23. Seismic and environmental controls on slow-moving landslides: Insights from the 2008 Wenchuan Earthquake H. Lu et al. https://doi.org/10.1016/j.enggeo.2024.107865
24. Statistically-based regional landslide susceptibility assessment in the UNESCO global geopark Caminhos dos Cânions do Sul (Brazil) M. Sugiyama et al. https://doi.org/10.1007/s11069-025-07478-8
25. Effectiveness of visually analyzing LiDAR DTM derivatives for earth and debris slide inventory mapping for statistical susceptibility modeling H. Petschko et al. https://doi.org/10.1007/s10346-015-0622-1
26. Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps S. Steger et al. https://doi.org/10.1016/j.geomorph.2016.03.015
27. Modeling the area of co-seismic landslides via data-driven models: The Kaikōura example M. Moreno et al. https://doi.org/10.1016/j.enggeo.2023.107121
28. An integrated approach for landslide susceptibility mapping by considering spatial correlation and fractal distribution of clustered landslide data L. Liu et al. https://doi.org/10.1007/s10346-018-01122-2
29. Combining release and runout in statistical landslide susceptibility modeling M. Mergili et al. https://doi.org/10.1007/s10346-019-01222-7
30. Landslide susceptibility and mobilization rates in the Mount Elgon region, Uganda J. Broeckx et al. https://doi.org/10.1007/s10346-018-1085-y
31. Towards the Use of Land Use Legacies in Landslide Modeling: Current Challenges and Future Perspectives in an Austrian Case Study R. Knevels et al. https://doi.org/10.3390/land10090954
32. Landslide susceptibility maps of Italy: Lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory M. Loche et al. https://doi.org/10.1016/j.earscirev.2022.104125
33. Detection and classification of active landslides along the Yellow River corridor using InSAR Y. Zhu et al. https://doi.org/10.1007/s10346-026-02785-y
34. Retrospective evaluation of landslide susceptibility maps and review of validation practice P. Fleuchaus et al. https://doi.org/10.1007/s12665-021-09770-9
35. Improvement of the predictive performance of landslide mapping models in mountainous terrains using cluster sampling M. Riaz et al. https://doi.org/10.1080/10106049.2022.2066202
36. Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster L. Lombardo et al. https://doi.org/10.1007/s00477-018-1518-0
37. The influence of cartographic representation on landslide susceptibility models: empirical evidence from a Brazilian UNESCO world heritage site J. Araujo Junior et al. https://doi.org/10.1007/s11069-024-06576-3
38. Delineation of subsurface variability in clay-rich landslides through spectral induced polarization imaging and electromagnetic methods J. Gallistl et al. https://doi.org/10.1016/j.enggeo.2018.09.001
39. Landslide Susceptibility Screening for Regional Investigation Prioritization: Integrating Ensemble Learning, Spatial Validation, Model Agreement, and Probability Uncertainty Y. Wu et al. https://doi.org/10.3390/su18189522
40. A novel perspective on fast landslide susceptibility mapping at regional scale: Probabilistic modelling and reliability updating B. Tong et al. https://doi.org/10.1007/s10346-026-02705-0
41. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques Z. Pourtaghi et al. https://doi.org/10.1016/j.ecolind.2015.12.030
42. The dilemma of determining the superiority of data mining models: optimal sampling balance and end users’ perspectives matter M. Teimouri & A. Kornejady https://doi.org/10.1007/s10064-019-01687-9
43. An ensemble landslide hazard model incorporating rainfall threshold for Mt. Umyeon, South Korea A. Pradhan et al. https://doi.org/10.1007/s10064-017-1055-y
44. Assessing uncertainties in landslide susceptibility predictions in a changing environment (Styrian Basin, Austria) R. Knevels et al. https://doi.org/10.5194/nhess-23-205-2023
45. The Use of High-Resolution Remote Sensing Data in Preparation of Input Data for Large-Scale Landslide Hazard Assessments M. Sinčić et al. https://doi.org/10.3390/land11081360
46. Application of a mass movement susceptibility model in the heterogeneous Miocene clastic successions of the Slovenj Gradec Basin, northeast Slovenia K. Ivančič et al. https://doi.org/10.3986/AGS.7040
47. Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning J. Dou et al. https://doi.org/10.1016/j.scitotenv.2020.137320
48. The influence of spatial patterns in rainfall on shallow landslides H. Smith et al. https://doi.org/10.1016/j.geomorph.2023.108795
49. Landslide susceptibility mapping using statistical and machine learning models (case study: Austria) M. Moharrami & M. Jelokhani-Niaraki https://doi.org/10.61186/jgst.13.2.79
50. Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy) A. Trigila et al. https://doi.org/10.1016/j.geomorph.2015.06.001
51. Susceptibility Assessment for Landslide Initiated along Power Transmission Lines S. Liu et al. https://doi.org/10.3390/rs13245068
52. Global research trends in landslides during 1991–2014: a bibliometric analysis X. Wu et al. https://doi.org/10.1007/s10346-015-0624-z
53. The influence of the inventory on the determination of the rainfall-induced shallow landslides susceptibility using generalized additive models M. Bordoni et al. https://doi.org/10.1016/j.catena.2020.104630
54. Landslide-risk scenario of the Costa Viola mountain ridge (Calabria, Southern Italy) O. Terranova et al. https://doi.org/10.1080/17445647.2016.1195300
55. Deciphering meteorological influencing factors for Alpine rockfalls: a case study in Aosta Valley G. Bajni et al. https://doi.org/10.1007/s10346-021-01697-3
56. A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods M. Sinčić et al. https://doi.org/10.3390/rs16162923
57. On the nexus between landslide susceptibility and transport infrastructure – an agent-based approach M. Schlögl et al. https://doi.org/10.5194/nhess-19-201-2019
58. National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data Q. Lin et al. https://doi.org/10.1016/j.gsf.2021.101248
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61. 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
62. Landslide Susceptibility Assessment Using Conditional Analysis and Rare Events Logistics Regression: A Case-Study in the Antrodoco Area (Rieti, Italy) V. Chiessi et al. https://doi.org/10.4236/gep.2016.412001
63. 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
64. Atmospheric rivers catalyze snowmelt and contribute to chains of landslides H. Aslan et al. https://doi.org/10.1016/j.catena.2025.109503
65. Mass movement susceptibility assessment of alpine infrastructure in the Salzkammergut area, Austria L. Abad et al. https://doi.org/10.1016/j.ijdrr.2022.103009
66. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling J. Lai et al. https://doi.org/10.3390/ijgi8090397
67. Quantifying effectiveness of trees for landslide erosion control R. Spiekermann et al. https://doi.org/10.1016/j.geomorph.2021.107993
68. Landslide Susceptibility Mapping Using Integrated Methods: A Case Study in the Chittagong Hilly Areas, Bangladesh Y. Rabby & Y. Li https://doi.org/10.3390/geosciences10120483
69. Scenario-based risk assessment of interrelating geomorphic multi-hazards - the critical road infrastructure of the Brenner Corridor T. Wenzel et al. https://doi.org/10.1016/j.nhres.2026.09.005
70. Integrating SBAS-InSAR and Machine Learning for Enhanced Landslide Identification and Susceptibility Mapping Along the West Kunlun Highway X. Dai et al. https://doi.org/10.3390/app16010120
71. Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai https://doi.org/10.1016/j.enggeo.2018.07.019
72. An Assessment of Negative Samples and Model Structures in Landslide Susceptibility Characterization Based on Bayesian Network Models S. Khabiri et al. https://doi.org/10.3390/rs15123200
73. Assessing the impact of inland navigation on the faecal pollution status of large rivers: A novel integrated field approach S. Steinbacher et al. https://doi.org/10.1016/j.watres.2024.122029
74. Applicability of spatial prediction models for landslide susceptibility in land-use zoning instruments: a guideline in a coastal settlement in South-Central Chile P. López et al. https://doi.org/10.1080/10106049.2021.1939440
75. Handling data imbalance in machine learning based landslide susceptibility mapping: a case study of Mandakini River Basin, North-Western Himalayas S. Gupta & D. Shukla https://doi.org/10.1007/s10346-022-01998-1
76. An ensemble model for landslide susceptibility mapping in a forested area A. Arabameri et al. https://doi.org/10.1080/10106049.2019.1585484
77. Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion Y. Garosi et al. https://doi.org/10.1016/j.scitotenv.2019.02.093
78. Risk analysis of forest roads using landslide susceptibility maps and generation of the optimum forest road route: a case study in Macka, Turkey F. Kadi et al. https://doi.org/10.1080/10106049.2019.1659424
79. Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping E. Kutlug Sahin & I. Colkesen https://doi.org/10.1080/10106049.2019.1641560
80. Landslide susceptibility modeling in a landslide prone area in Mazandarn Province, north of Iran: a comparison between GLM, GAM, MARS, and M-AHP methods H. Pourghasemi & M. Rossi https://doi.org/10.1007/s00704-016-1919-2
81. Landslide Susceptibility Assessment Tools v1.0.0b – Project Manager Suite: a new modular toolkit for landslide susceptibility assessment J. Torizin et al. https://doi.org/10.5194/gmd-15-2791-2022
82. Population dynamics of forest-forming tree species on landslide-scree slopes of the right bank of the Kuibyshev reservoir (Tatarstan) N. Kotova et al. https://doi.org/10.1088/1755-1315/1070/1/012007
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85. Sensitivity and Uncertainty Analysis of Statistical and Machine Learning based Landslide Susceptibility Mapping in a Part of Western Ghats, India P. Kundu et al. https://doi.org/10.1007/s40098-026-01570-x
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95. Landslide susceptibility assessment using weighted linear combination and InSAR with emphasis on tectonic influences: Insights from Salas Babajani County, western Iran H. Ganjaeian et al. https://doi.org/10.1016/j.rines.2026.100169
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124. Landslide hazard mapping of Ibrahim River Basin, Lebanon C. Abdallah & G. Faour https://doi.org/10.1007/s11069-016-2560-1
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147. Landslide susceptibility modelling using the quantitative random forest method along the northern portion of the Yukon Alaska Highway Corridor, Canada P. Behnia & A. Blais-Stevens https://doi.org/10.1007/s11069-017-3104-z
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