196 citations as recorded by crossref.

1. Dynamic spatial modelling of mass movement impacts for large areas: a data-driven framework for impact-based early warning S. Steger et al. https://doi.org/10.5194/nhess-26-3637-2026
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4. Determination of alternative forest road routes using produced landslide susceptibility maps: A case study of Tonya (Trabzon), Türkiye F. Kadı & O. Yılmaz https://doi.org/10.26833/ijeg.1355615
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6. Application of machine learning methods for snow avalanche susceptibility mapping in the Parlung Tsangpo catchment, southeastern Qinghai-Tibet Plateau H. Wen et al. https://doi.org/10.1016/j.coldregions.2022.103535
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8. Assessing Landslide Susceptibility along India’s National Highway 58: A Comprehensive Approach Integrating Remote Sensing, GIS, and Logistic Regression Analysis M. Sharma et al. https://doi.org/10.3390/conservation3030030
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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
13. Evaluating logistic regression and decision tree for landslide susceptibility mapping: A comparison with heuristic methods in Indonesia K. Rama et al. https://doi.org/10.15243/jdmlm.2026.133.10307
14. Conventional data-driven landslide susceptibility models may only tell us half of the story: Potential underestimation of landslide impact areas depending on the modeling design P. Lima et al. https://doi.org/10.1016/j.geomorph.2023.108638
15. Flood susceptibility assessment using extreme gradient boosting (EGB), Iran S. Mirzaei et al. https://doi.org/10.1007/s12145-020-00530-0
16. Integrating physics-guided Persistent Scatterer Interferometry (PS-InSAR) derived indices and machine learning for landslide susceptibility mapping D. Khadka et al. https://doi.org/10.1016/j.asr.2026.08.058
17. Presence-only approach to assess landslide triggering-thickness susceptibility: a test for the Mili catchment (north-eastern Sicily, Italy) L. Lombardo et al. https://doi.org/10.1007/s11069-016-2443-5
18. On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values N. Wang et al. https://doi.org/10.1016/j.gsf.2024.101800
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. Combining release and runout in statistical landslide susceptibility modeling M. Mergili et al. https://doi.org/10.1007/s10346-019-01222-7
29. Landslide susceptibility and mobilization rates in the Mount Elgon region, Uganda J. Broeckx et al. https://doi.org/10.1007/s10346-018-1085-y
30. 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
31. 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
32. 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
33. Retrospective evaluation of landslide susceptibility maps and review of validation practice P. Fleuchaus et al. https://doi.org/10.1007/s12665-021-09770-9
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. The influence of spatial patterns in rainfall on shallow landslides H. Smith et al. https://doi.org/10.1016/j.geomorph.2023.108795
48. 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
49. 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
50. Susceptibility Assessment for Landslide Initiated along Power Transmission Lines S. Liu et al. https://doi.org/10.3390/rs13245068
51. Global research trends in landslides during 1991–2014: a bibliometric analysis X. Wu et al. https://doi.org/10.1007/s10346-015-0624-z
52. 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
53. Landslide-risk scenario of the Costa Viola mountain ridge (Calabria, Southern Italy) O. Terranova et al. https://doi.org/10.1080/17445647.2016.1195300
54. 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
55. 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
56. 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
57. 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
58. Space-time landslide predictive modelling L. Lombardo et al. https://doi.org/10.1016/j.earscirev.2020.103318
59. Role of landslide sampling strategies in susceptibility modelling: types, comparison and mechanism J. Thanveer et al. https://doi.org/10.1007/s10064-024-03851-2
60. 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
61. 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
62. 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
63. Atmospheric rivers catalyze snowmelt and contribute to chains of landslides H. Aslan et al. https://doi.org/10.1016/j.catena.2025.109503
64. 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
65. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling J. Lai et al. https://doi.org/10.3390/ijgi8090397
66. Quantifying effectiveness of trees for landslide erosion control R. Spiekermann et al. https://doi.org/10.1016/j.geomorph.2021.107993
67. 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
68. 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
69. Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai https://doi.org/10.1016/j.enggeo.2018.07.019
70. 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
71. 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
72. 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
73. 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
74. An ensemble model for landslide susceptibility mapping in a forested area A. Arabameri et al. https://doi.org/10.1080/10106049.2019.1585484
75. 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
76. 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
77. 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
78. 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
79. 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
80. 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
81. Integration of information value with machine learning method for an enhanced predictive performance in landslide susceptibility mapping B. Joshi et al. https://doi.org/10.1007/s44288-026-00517-2
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83. 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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85. Project ‘‘Mass Movements in Germany’’ and its implications for nationwide landslide susceptibility assessment J. Torizin et al. https://doi.org/10.1007/s10064-024-03691-0
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93. 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
94. The “Salcher” landslide observatory—experimental long-term monitoring in the Flysch Zone of Lower Austria M. Stumvoll et al. https://doi.org/10.1007/s10064-019-01632-w
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101. 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
102. Landslide susceptibility modeling in the Kulfo river catchment, rift valley, Ethiopia: An integrated geospatial and statistical analysis T. Mulugeta et al. https://doi.org/10.1016/j.qsa.2024.100191
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105. Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy) M. Cama et al. https://doi.org/10.5194/nhess-15-1785-2015
106. Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution A. Shirzadi et al. https://doi.org/10.1016/j.catena.2019.03.017
107. Mapping the susceptibility of rainfall and earthquake triggered landslides along China–Nepal highways K. Gnyawali et al. https://doi.org/10.1007/s10064-019-01583-2
108. Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on Artificial Neural Networks L. Bragagnolo et al. https://doi.org/10.1016/j.envsoft.2019.104565
109. 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
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112. Landslide susceptibility mapping using an ensemble model of Bagging scheme and random subspace–based naïve Bayes tree in Zigui County of the Three Gorges Reservoir Area, China X. Hu et al. https://doi.org/10.1007/s10064-021-02275-6
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115. A test of transferability for landslides susceptibility models under extreme climatic events: application to the Messina 2009 disaster L. Lombardo et al. https://doi.org/10.1007/s11069-014-1285-2
116. From spatio-temporal landslide susceptibility to landslide risk forecast T. Wang et al. https://doi.org/10.1016/j.gsf.2023.101765
117. Space-time susceptibility modeling of hydro-morphological processes at the Chinese national scale N. Wang et al. https://doi.org/10.1016/j.enggeo.2022.106586
118. Landslide Susceptibility Mapping for Austria Using Geons and Optimization with the Dempster-Shafer Theory T. Gudiyangada Nachappa et al. https://doi.org/10.3390/app9245393
119. Rock fall susceptibility assessment along a mountainous road: an evaluation of bivariate statistic, analytical hierarchy process and frequency ratio A. Shirzadi et al. https://doi.org/10.1007/s12665-017-6471-6
120. Landslide hazard mapping of Ibrahim River Basin, Lebanon C. Abdallah & G. Faour https://doi.org/10.1007/s11069-016-2560-1
121. Comparing methods of landslide data acquisition and susceptibility modelling: Examples from New Zealand H. Smith et al. https://doi.org/10.1016/j.geomorph.2021.107660
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123. A global slope unit-based method for the near real-time prediction of earthquake-induced landslides H. Tanyas et al. https://doi.org/10.1016/j.geomorph.2018.10.022
124. Transferability of regional permafrost disturbance susceptibility modelling using generalized linear and generalized additive models A. Rudy et al. https://doi.org/10.1016/j.geomorph.2016.04.011
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127. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model H. Hussin et al. https://doi.org/10.1016/j.geomorph.2015.10.030
128. Terrain Controls and Landscape-Scale Susceptibility Modelling of Active-Layer Detachments, Sabine Peninsula, Melville Island, Nunavut A. Rudy et al. https://doi.org/10.1002/ppp.1900
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132. Rainfall-Induced Landslide Detection in the Central–Southern Andes of Chile Using Integrated SAR, Optical, and Machine-Learning Approaches in Google Earth Engine L. Gajardo et al. https://doi.org/10.1109/JSTARS.2026.3716900
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135. Common and type-specific geo-environmental controls of landslide-type variability: A multivariate analysis in the Rif Mountains, Morocco M. Hamidi et al. https://doi.org/10.1016/j.geomorph.2026.110512
136. Estimating global landslide susceptibility and its uncertainty through ensemble modeling A. Felsberg et al. https://doi.org/10.5194/nhess-22-3063-2022
137. An inventory-driven rock glacier status model (intact vs. relict) for South Tyrol, Eastern Italian Alps C. Kofler et al. https://doi.org/10.1016/j.geomorph.2019.106887
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140. The added value of a regional landslide susceptibility assessment: The western branch of the East African Rift A. Depicker et al. https://doi.org/10.1016/j.geomorph.2019.106886
141. Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning Z. Wang et al. https://doi.org/10.5194/gmd-15-8765-2022
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143. 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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148. Spatial landslide prediction with limited inventories using machine learning coupled with generative and explainable models H. Nguyen et al. https://doi.org/10.1016/j.asr.2026.07.086
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