138 citations as recorded by crossref.

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8. 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
9. Avalanche size estimation and avalanche outline determination by experts: reliability and implications for practice E. Hafner et al. https://doi.org/10.5194/nhess-23-2895-2023
10. Seasonal landslide mapping and estimation of landslide mobilization rates using aerial and satellite images F. Fiorucci et al. https://doi.org/10.1016/j.geomorph.2011.01.013
11. Landslide Susceptibility Mapping Using GIS-Based Data Mining Algorithms V. Vakhshoori et al. https://doi.org/10.3390/w11112292
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13. RS & GIS-based landslide susceptibility mapping of the Balason River basin, Darjeeling Himalaya, using logistic regression (LR) model S. Mondal & S. Mandal https://doi.org/10.1080/17499518.2017.1347949
14. Exploring event landslide mapping using Sentinel-1 SAR backscatter products M. Santangelo et al. https://doi.org/10.1016/j.geomorph.2021.108021
15. Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory J. Samia et al. https://doi.org/10.1007/s10346-016-0739-x
16. Research on Uncertainty of Landslide Susceptibility Prediction—Bibliometrics and Knowledge Graph Analysis Z. Yang et al. https://doi.org/10.3390/rs14163879
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18. Detecting and Characterizing Active Thrust Fault and Deep-Seated Landslides in Dense Forest Areas of Southern Taiwan Using Airborne LiDAR DEM R. Chen et al. https://doi.org/10.3390/rs71115443
19. Recommendations for the quantitative analysis of landslide risk J. Corominas et al. https://doi.org/10.1007/s10064-013-0538-8
20. Techniques for evaluating the performance of landslide susceptibility models P. Frattini et al. https://doi.org/10.1016/j.enggeo.2009.12.004
21. Correlación de variables morfométricas para deslizamientos en la cuenca del río Combeima, Colombia G. Santa-Ramírez et al. https://doi.org/10.17230/ingciencia.16.31.7
22. Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study L. Li & H. Lan https://doi.org/10.3390/ijerph17218055
23. Active Learning in the Spatial Domain for Remote Sensing Image Classification A. Stumpf et al. https://doi.org/10.1109/TGRS.2013.2262052
24. Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling – Benefits of exploring landslide data collection effects S. Steger et al. https://doi.org/10.1016/j.scitotenv.2021.145935
25. Comparing landslide inventory maps M. Galli et al. https://doi.org/10.1016/j.geomorph.2006.09.023
26. A global landslide catalog for hazard applications: method, results, and limitations D. Kirschbaum et al. https://doi.org/10.1007/s11069-009-9401-4
27. Geographic object-based image analysis for landslide identification using machine learning on google earth engine D. Khadka et al. https://doi.org/10.1007/s12665-024-12045-8
28. Engineering geology maps: landslides and geographical information systems J. Chacón et al. https://doi.org/10.1007/s10064-006-0064-z
29. Optimal landslide susceptibility zonation based on multiple forecasts M. Rossi et al. https://doi.org/10.1016/j.geomorph.2009.06.020
30. Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria P. Lima et al. https://doi.org/10.1007/s10346-021-01693-7
31. A review on spatial, temporal and magnitude prediction of landslide hazard A. Tyagi et al. https://doi.org/10.1016/j.jaesx.2022.100099
32. Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility P. Lima et al. https://doi.org/10.1007/s11629-021-7254-9
33. An epidemiological approach to determining the risk of road damage due to landslides M. Bíl et al. https://doi.org/10.1007/s11069-014-1141-4
34. Landslide susceptibility assessment by bivariate methods at large scales: Application to a complex mountainous environment Y. Thiery et al. https://doi.org/10.1016/j.geomorph.2007.02.020
35. Developing an algorithm for automated geometric analysis and classification of landslides incorporating LiDAR-derived DEM S. Pirasteh & J. Li https://doi.org/10.1007/s12665-018-7583-3
36. Regional susceptibility assessments with heterogeneous landslide information: Slope unit- vs. pixel-based approach L. Jacobs et al. https://doi.org/10.1016/j.geomorph.2020.107084
37. Investigating Spatial Patterns of Persistent Scatterer Interferometry Point Targets and Landslide Occurrences in the Arno River Basin P. Lu et al. https://doi.org/10.3390/rs6086817
38. 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
39. Assessment and validation of GIS-based landslide susceptibility maps: a case study from Feltrino stream basin (Central Italy) M. Sciarra et al. https://doi.org/10.1007/s10064-016-0954-7
40. The Application of Fast Fourier Transform Filtering to High Spatial Resolution Digital Terrain Models Derived from LiDAR Sensors for the Objective Mapping of Surface Features and Digital Terrain Model Evaluations A. González-Díez et al. https://doi.org/10.3390/rs17010150
41. Urban and Rural Landslide Hazard and Exposure Mapping Using Landsat and Corona Satellite Imagery for Tehran and the Alborz Mountains, Iran A. Fekete https://doi.org/10.3934/geosci.2017.1.37
42. Statistical and Geomatic Approaches to Typological Characterization and Susceptibility Mapping of Mass Movements in Northwestern Morocco’s Alpine Zone M. Mastere et al. https://doi.org/10.3390/geomatics5040051
43. Landslide hazard assessment of the 2008 Wenchuan earthquake: a case study in Beichuan area C. Tang et al. https://doi.org/10.1139/T10-059
44. Generation of a landslide risk index map for Cuba using spatial multi-criteria evaluation E. Abella & C. Van Westen https://doi.org/10.1007/s10346-007-0087-y
45. Integrating Landslide Typology with Weighted Frequency Ratio Model for Landslide Susceptibility Mapping: A Case Study from Lanzhou City of Northwestern China H. Shu et al. https://doi.org/10.3390/rs13183623
46. Characteristics of landslides and assessment of deep-seated landslide susceptibility in Northern Turkey T. Duman & T. Çan https://doi.org/10.1007/s42990-023-00105-3
47. Delineation of groundwater potential zones and recharge using multi-source big data and systematic analysis approach O. Rahim et al. https://doi.org/10.1016/j.sftr.2025.100872
48. Landslide susceptibility methodology for railway planning: a comparative analysis of statistical and machine learning methods in a case study of Marche region, Italy R. Rani et al. https://doi.org/10.1016/j.trgeo.2025.101731
49. Artificial neural networks and cluster analysis in landslide susceptibility zonation C. Melchiorre et al. https://doi.org/10.1016/j.geomorph.2006.10.035
50. Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography C. Lin et al. https://doi.org/10.1016/j.jseaes.2012.10.022
51. Mapping landslide susceptibility using data-driven methods J. Zêzere et al. https://doi.org/10.1016/j.scitotenv.2017.02.188
52. Using Artificial Neural Networks and Spectral Indices to Predict Water Availability in New Capital (IKN) and Its’ Surroundings N. Arif & L. Toersilowati https://doi.org/10.1007/s12524-024-01889-z
53. The influence of forest cover on landslide occurrence explored with spatio-temporal information E. Schmaltz et al. https://doi.org/10.1016/j.geomorph.2017.04.024
54. Updating Inventory, Deformation, and Development Characteristics of Landslides in Hunza Valley, NW Karakoram, Pakistan by SBAS-InSAR X. Su et al. https://doi.org/10.3390/rs14194907
55. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling J. Lai et al. https://doi.org/10.3390/ijgi8090397
56. Using Landslide Statistical Index Technique for Landslide Susceptibility Mapping: Case Study: Ban Khoang Commune, Lao Cai Province, Vietnam L. Thanh et al. https://doi.org/10.3390/w14182814
57. Landslide Susceptibility and Soil Loss Estimates for Drift Creek Watershed, Lincoln County, Oregon D. Korte & A. Shakoor https://doi.org/10.2113/EEG-2251
58. MAS-LAND: A multi-agent system for landslide detection and rapid response A. Riche et al. https://doi.org/10.1016/j.ijdrr.2026.106263
59. The (f)utility to account for pre-failure topography in data-driven landslide susceptibility modelling S. Steger et al. https://doi.org/10.1016/j.geomorph.2020.107041
60. Impact and a Novel Representation of Spatial Data Uncertainty in Debris Flow Susceptibility Analysis L. Kurilla & G. Fubelli https://doi.org/10.3390/app12136697
61. Application of frequency ratio (FR) model in spatial prediction of landslides in the Balason river basin, Darjeeling Himalaya S. Mondal & S. Mandal https://doi.org/10.1007/s41324-017-0101-y
62. A multi-annual landslide inventory for the assessment of shallow landslide susceptibility – Two test cases in Vorarlberg, Austria T. Zieher et al. https://doi.org/10.1016/j.geomorph.2016.02.008
63. The effectiveness of hillshade maps and expert knowledge in mapping old deep-seated landslides M. Van Den Eeckhaut et al. https://doi.org/10.1016/j.geomorph.2004.11.001
64. Producing landslide-susceptibility maps for regional planning in data-scarce regions J. De Graff et al. https://doi.org/10.1007/s11069-012-0267-5
65. Deep convolutional neural network–based pixel-wise landslide inventory mapping Z. Su et al. https://doi.org/10.1007/s10346-020-01557-6
66. Quality assessment of the Italian Landslide Inventory using GIS processing A. Trigila et al. https://doi.org/10.1007/s10346-010-0213-0
67. Comparing Manual and Semi-Automated Landslide Mapping Based on Optical Satellite Images from Different Sensors D. Hölbling et al. https://doi.org/10.3390/geosciences7020037
68. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues F. Catani et al. https://doi.org/10.5194/nhess-13-2815-2013
69. 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
70. Improving the reliability of landslide susceptibility mapping through spatial uncertainty analysis: a case study of Al Hoceima, Northern Morocco H. Rahali https://doi.org/10.1080/10106049.2017.1357767
71. Semiautomatic inventory and geomorphological characterization of mass movements using high-resolution images and open-source software in the Ribeira de Iguape Valley, Brazil J. Bonini et al. https://doi.org/10.1016/j.jsames.2022.104029
72. A comparative modeling of landslides susceptibility at a meso-scale using frequency ratio and analytic hierarchy process models in geographic information system: the case of African Alpine Mountains (Rif, Morocco) N. Boukhres et al. https://doi.org/10.1007/s40808-022-01605-1
73. Landslide susceptibility: a statistically-based assessment on a depositional pyroclastic ramp F. Murillo-García et al. https://doi.org/10.1007/s11629-018-5225-6
74. 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
75. Detecting fingerprints of landslide drivers: A MaxEnt model M. Convertino et al. https://doi.org/10.1002/jgrf.20099
76. Guidelines for landslide susceptibility, hazard and risk zoning for land use planning R. Fell et al. https://doi.org/10.1016/j.enggeo.2008.03.022
77. Landslide Susceptibility Zone using Frequency Ratio Method in Karangkobar Catchment, Merawu Watershed, Banjarnegara District, Central Java Province V. Radjah et al. https://doi.org/10.1088/1755-1315/451/1/012087
78. An integrated framework to identify and analyze karst sinkholes V. Zumpano et al. https://doi.org/10.1016/j.geomorph.2019.02.013
79. Regional landslide susceptibility zoning with considering the aggregation of landslide points and the weights of factors X. Wang et al. https://doi.org/10.1007/s10346-013-0392-6
80. Deterministic modeling of landslide and rockfall risk M. Zorn & B. Komac https://doi.org/10.3986/AGS44203
81. Which data for quantitative landslide susceptibility mapping at operational scale? Case study of the Pays d'Auge plateau hillslopes (Normandy, France) M. Fressard et al. https://doi.org/10.5194/nhess-14-569-2014
82. A Novel Historical Landslide Detection Approach Based on LiDAR and Lightweight Attention U-Net C. Fang et al. https://doi.org/10.3390/rs14174357
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84. Metodología para la zonificación de la susceptibilidad por movimientos en masa en proyectos lineales. Estudio de caso en elacueducto del municipio de Fredonia,Antioquia J. Mendoza Ramirez & E. Aristizábal Giraldo https://doi.org/10.17230/ingciencia.13.26.7
85. Semiautomatic Inventory and Geomorphological Characterization of Mass Movements Using High-Resolution Images and Open Source Software in the Ribeira De Iguape Valley, Brazil J. Bonini et al. https://doi.org/10.2139/ssrn.4159904
86. LAND-SE: a software for statistically based landslide susceptibility zonation, version 1.0 M. Rossi & P. Reichenbach https://doi.org/10.5194/gmd-9-3533-2016
87. Effects of forest cover, topography, and sampling extent on the measured density of shallow, translational landslides D. Miller & K. Burnett https://doi.org/10.1029/2005WR004807
88. A landslide database for Nicaragua: a tool for landslide-hazard management G. Devoli et al. https://doi.org/10.1007/s10346-006-0074-8
89. Landslide Representation Strategies in Susceptibility Studies using Weights-of-Evidence Modeling Technique S. Poli & S. Sterlacchini https://doi.org/10.1007/s11053-007-9043-8
90. Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: A case of the Belice River basin (western Sicily, Italy) C. Conoscenti et al. https://doi.org/10.1016/j.geomorph.2014.09.020
91. 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
92. Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity X. Deng et al. https://doi.org/10.3390/ijgi6040103
93. Spatio-temporal evolution of mass wasting after the 2008 Mw 7.9 Wenchuan earthquake revealed by a detailed multi-temporal inventory X. Fan et al. https://doi.org/10.1007/s10346-018-1054-5
94. Historical geospatial database for landslide analysis: the Catalogue of Landslide OCcurrences in the Emilia-Romagna Region (CLOCkER) D. Piacentini et al. https://doi.org/10.1007/s10346-018-0962-8
95. Automated determination of landslide locations after large trigger events: advantages and disadvantages compared to manual mapping D. Milledge et al. https://doi.org/10.5194/nhess-22-481-2022
96. Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study Á. Felicísimo et al. https://doi.org/10.1007/s10346-012-0320-1
97. Landslide susceptibility assessment on a large scale in the Podsljeme area, City of Zagreb (Croatia) S. Bernat Gazibara et al. https://doi.org/10.1080/17445647.2022.2163197
98. Scaling and multivariate analysis of medium to large landslide events: Haida Gwaii, British Columbia L. Jagielko et al. https://doi.org/10.1007/s11069-011-0012-5
99. Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park, Ohio P. Gorsevski et al. https://doi.org/10.1007/s10346-015-0587-0
100. Space–time landslide hazard modeling via Ensemble Neural Networks A. Dahal et al. https://doi.org/10.5194/nhess-24-823-2024
101. Landslide hazard assessment and judgment of reliability: a geomechanical approach F. Cafaro et al. https://doi.org/10.1007/s10064-016-0966-3
102. Landslide databases for climate change detection and attribution J. Wood et al. https://doi.org/10.1016/j.geomorph.2020.107061
103. Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers M. Benbouras https://doi.org/10.1016/j.ijsrc.2022.04.003
104. Landslide susceptibility–certainty mapping by a multi-method approach: A case study in the Tertiary basin of Puy-en-Velay (Massif central, France) A. Poiraud https://doi.org/10.1016/j.geomorph.2014.04.001
105. Improved landslide susceptibility prediction for sustainable forest management in an altered climate M. Barik et al. https://doi.org/10.1016/j.enggeo.2017.09.026
106. Application of a fuzzy verification framework for the evaluation of a regional-scale landslide early warning system during the January 2020 Gloria storm in Catalonia (NE Spain) R. Palau et al. https://doi.org/10.1007/s10346-022-01854-2
107. Commentaire de la carte géomorphologique du bassin versant du Brunet N. Bollot https://doi.org/10.4000/mappemonde.2387
108. Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning R. Fell et al. https://doi.org/10.1016/j.enggeo.2008.03.014
109. Landslide zoning over large areas from a sample inventory by means of scale-dependent terrain units M. Calvello et al. https://doi.org/10.1016/j.geomorph.2012.10.026
110. Attribute selection using correlations and principal components for artificial neural networks employment for landslide susceptibility assessment L. Lucchese et al. https://doi.org/10.1007/s10661-019-7968-0
111. GIS-based landslide susceptibility mapping of Western Rwanda: an integrated artificial neural network, frequency ratio, and Shannon entropy approach V. Nwazelibe et al. https://doi.org/10.1007/s12665-023-11134-4
112. Integrated Geomatics Surveying and Data Management in the Investigation of Slope and Fluvial Dynamics C. Parenti et al. https://doi.org/10.3390/geosciences12080293
113. The influence of systematically incomplete shallow landslide inventories on statistical susceptibility models and suggestions for improvements S. Steger et al. https://doi.org/10.1007/s10346-017-0820-0
114. A landslide susceptibility model using the Analytical Hierarchy Process method and multivariate statistics in perialpine Slovenia M. Komac https://doi.org/10.1016/j.geomorph.2005.07.005
115. The propagation of inventory-based positional errors into statistical landslide susceptibility models S. Steger et al. https://doi.org/10.5194/nhess-16-2729-2016
116. Introduction to a thematic set of papers on methods to assess the reliability of landslide hazard mapping G. Vessia et al. https://doi.org/10.1007/s10064-017-1026-3
117. Shallow landslide susceptibility map at a regional scale (Asturias, NW Spain). A heuristic-driven approach R. Menéndez-Duarte et al. https://doi.org/10.1080/17445647.2024.2375094
118. Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows T. Heckmann et al. https://doi.org/10.5194/nhess-14-259-2014
119. Comparison of rockfall susceptibility assessment at local and regional scale: a case study in the north of Beijing (China) X. Wang et al. https://doi.org/10.1007/s12665-014-3718-3
120. Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling J. Goetz et al. https://doi.org/10.5194/nhess-21-2543-2021
121. Automatic landslide length and width estimation based on the geometric processing of the bounding box and the geomorphometric analysis of DEMs M. Niculiţǎ https://doi.org/10.5194/nhess-16-2021-2016
122. Probability modelling of landslide hazard M. Zorn & B. Komac https://doi.org/10.3986/AGS47201
123. Assessing the quality of landslide susceptibility maps – case study Lower Austria H. Petschko et al. https://doi.org/10.5194/nhess-14-95-2014
124. An integrated landslide susceptibility assessment in the Karakoram Mountains based on SBAS-InSAR and machine learning: a case study of the Hunza Valley X. Su et al. https://doi.org/10.1007/s10064-025-04299-8
125. An approach to reduce mapping errors in the production of landslide inventory maps M. Santangelo et al. https://doi.org/10.5194/nhess-15-2111-2015
126. A new algorithm for landslide geometric and deformation analysis supported by digital elevation models S. Pirasteh et al. https://doi.org/10.1007/s12145-019-00437-5
127. Comparing models of debris-flow susceptibility in the alpine environment A. Carrara et al. https://doi.org/10.1016/j.geomorph.2006.10.033
128. GIS-based landslide hazard predicting system and its real-time test during a typhoon, Zhejiang Province, Southeast China Y. Wu et al. https://doi.org/10.1016/j.enggeo.2014.03.005
129. Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study R. Schlögel et al. https://doi.org/10.5194/nhess-15-2369-2015
130. Stability Analysis of a Landslide: A View with Implications of Microstructural Soil Characters F. Sunbul et al. https://doi.org/10.1007/s40098-020-00467-7
131. Geomorphologic landslide inventory by air photo interpretation of the High Agri Valley (Southern Italy) F. Bucci et al. https://doi.org/10.1080/17445647.2021.1943552
132. Landslide susceptibility assessment framework in the central Rif chain, Northern Morocco O. Obda et al. https://doi.org/10.1007/s42990-025-00216-z
133. Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview C. van Westen et al. https://doi.org/10.1016/j.enggeo.2008.03.010
134. The use of filters based on the Fast Fourier Transform applied to DEMs for the objective mapping of karstic features A. González-Díez et al. https://doi.org/10.1016/j.geomorph.2021.107724
135. Probabilistic landslide hazard assessment at the basin scale F. Guzzetti et al. https://doi.org/10.1016/j.geomorph.2005.06.002
136. Modelling multi-hazard threats to cultural heritage sites and environmental sustainability: The present and future scenarios A. Saha et al. https://doi.org/10.1016/j.jclepro.2021.128713
137. 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
138. Multi-method characterisation of an active landslide: Case study in the Pays d'Auge plateau (Normandy, France) M. Fressard et al. https://doi.org/10.1016/j.geomorph.2016.07.001