188 citations as recorded by crossref.

1. The influence of sampling on landslide susceptibility mapping using artificial neural networks S. Gameiro et al. https://doi.org/10.1080/10106049.2022.2144475
2. Geoenvironmental conditioning of landsliding in river valleys of lowland regions and its significance in landslide susceptibility assessment: A case study in the Lower Vistula Valley, Northern Poland D. Grabowski et al. https://doi.org/10.1016/j.geomorph.2022.108490
3. 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
4. Introducing the coupled stepwise areal constraining and Mahalanobis distance: a promising MCDM-based probabilistic model for landfill site selection D. Davoudi Moghaddam et al. https://doi.org/10.1007/s11356-020-08746-9
5. 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
6. Internal structure of an active landslide based on ERT and DP data: New insights from the Hofermühle landslide observatory in Lower Austria J. Luhn et al. https://doi.org/10.1016/j.geomorph.2023.108910
7. 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
8. Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling J. Goetz et al. https://doi.org/10.1016/j.cageo.2015.04.007
9. 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
10. Investigating causal factors of shallow landslides in grassland regions of Switzerland L. Zweifel et al. https://doi.org/10.5194/nhess-21-3421-2021
11. 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
12. 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
13. 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
14. Flood susceptibility assessment using extreme gradient boosting (EGB), Iran S. Mirzaei et al. https://doi.org/10.1007/s12145-020-00530-0
15. 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
16. 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
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. Combining release and runout in statistical landslide susceptibility modeling M. Mergili et al. https://doi.org/10.1007/s10346-019-01222-7
27. Landslide susceptibility and mobilization rates in the Mount Elgon region, Uganda J. Broeckx et al. https://doi.org/10.1007/s10346-018-1085-y
28. 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
29. 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
30. 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
31. Retrospective evaluation of landslide susceptibility maps and review of validation practice P. Fleuchaus et al. https://doi.org/10.1007/s12665-021-09770-9
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. The influence of spatial patterns in rainfall on shallow landslides H. Smith et al. https://doi.org/10.1016/j.geomorph.2023.108795
45. 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
46. 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
47. Susceptibility Assessment for Landslide Initiated along Power Transmission Lines S. Liu et al. https://doi.org/10.3390/rs13245068
48. Global research trends in landslides during 1991–2014: a bibliometric analysis X. Wu et al. https://doi.org/10.1007/s10346-015-0624-z
49. 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
50. Landslide-risk scenario of the Costa Viola mountain ridge (Calabria, Southern Italy) O. Terranova et al. https://doi.org/10.1080/17445647.2016.1195300
51. 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
52. 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
53. 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
54. 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
55. Space-time landslide predictive modelling L. Lombardo et al. https://doi.org/10.1016/j.earscirev.2020.103318
56. 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
57. 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
58. 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
59. 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
60. Atmospheric rivers catalyze snowmelt and contribute to chains of landslides H. Aslan et al. https://doi.org/10.1016/j.catena.2025.109503
61. 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
62. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling J. Lai et al. https://doi.org/10.3390/ijgi8090397
63. Quantifying effectiveness of trees for landslide erosion control R. Spiekermann et al. https://doi.org/10.1016/j.geomorph.2021.107993
64. 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
65. 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
66. Presenting logistic regression-based landslide susceptibility results L. Lombardo & P. Mai https://doi.org/10.1016/j.enggeo.2018.07.019
67. 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
68. 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
69. 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
70. 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
71. An ensemble model for landslide susceptibility mapping in a forested area A. Arabameri et al. https://doi.org/10.1080/10106049.2019.1585484
72. 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
73. 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
74. 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
75. 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
76. 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
77. 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
78. 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
79. Generating accurate negative samples for landslide susceptibility mapping: A combined self-organizing-map and one-class SVM method C. Ye et al. https://doi.org/10.3389/feart.2022.1054027
80. 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
81. Event-based landslide susceptibility models in Shihmen watershed, Taiwan: accounting for the characteristics of rainfall events C. Wu & S. Lin https://doi.org/10.1007/s10661-022-10075-y
82. 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
83. Mapping Susceptibility With Open-Source Tools: A New Plugin for QGIS G. Titti et al. https://doi.org/10.3389/feart.2022.842425
84. Multivariate geostatistical modeling of the spatial sediment distribution in a large scale drainage basin, Upper Rhone, Switzerland A. Schoch et al. https://doi.org/10.1016/j.geomorph.2017.11.026
85. Cluster-Based Interpretable Machine Learning for Landslide Susceptibility Mapping: A Case Study in Northern Guangdong Z. Qing et al. https://doi.org/10.3390/su18126347
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. Examining the Controls on the Spatial Distribution of Landslides Triggered by the 2008 Wenchuan Ms 8.0 Earthquake, China, Using Methods of Spatial Point Pattern Analysis G. Bai et al. https://doi.org/10.3390/su16166974
88. When Enough Is Really Enough? On the Minimum Number of Landslides to Build Reliable Susceptibility Models G. Titti et al. https://doi.org/10.3390/geosciences11110469
89. Slope unit-based evaluation of climate change impacts on landslide susceptibility in the Nepal Himalaya T. Bhattarai & N. Bhandary https://doi.org/10.1080/19475705.2026.2649615
90. 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
91. 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
92. Data‐driven modelling of joint debris flow release susceptibility and connectivity S. Steger et al. https://doi.org/10.1002/esp.5421
93. Seismic detection of rockslides at regional scale: examples from the Eastern Alps and feasibility of kurtosis-based event location F. Fuchs et al. https://doi.org/10.5194/esurf-6-955-2018
94. Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology E. Canli et al. https://doi.org/10.5194/nhess-18-2183-2018
95. Remote sensing and GIS-based landslide susceptibility mapping using LNRF method in part of Western Ghats of India A. Patil & S. Panhalkar https://doi.org/10.1016/j.qsa.2023.100095
96. 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
97. Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification D. Xia et al. https://doi.org/10.3390/rs14112707
98. 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
99. 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
100. Building a landslide hazard indicator with machine learning and land surface models T. Stanley et al. https://doi.org/10.1016/j.envsoft.2020.104692
101. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance A. Merghadi et al. https://doi.org/10.1016/j.earscirev.2020.103225
102. 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
103. 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
104. 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
105. 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
106. 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
107. Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds S. Steger et al. https://doi.org/10.1016/j.gsf.2024.101822
108. Towards a holistic assessment of landslide susceptibility models: insights from the Central Eastern Alps M. Schlögl et al. https://doi.org/10.1007/s12665-024-12041-y
109. 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
110. A review of statistically-based landslide susceptibility models P. Reichenbach et al. https://doi.org/10.1016/j.earscirev.2018.03.001
111. Mapping landslide susceptibility at national scale by spatial multi-criteria evaluation A. Grozavu & C. Patriche https://doi.org/10.1080/19475705.2021.1914752
112. 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
113. From spatio-temporal landslide susceptibility to landslide risk forecast T. Wang et al. https://doi.org/10.1016/j.gsf.2023.101765
114. 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
115. 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
116. 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
117. Landslide hazard mapping of Ibrahim River Basin, Lebanon C. Abdallah & G. Faour https://doi.org/10.1007/s11069-016-2560-1
118. 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
119. Brief communication: Visualizing uncertainties in landslide susceptibility modelling using bivariate mapping M. Schlögl et al. https://doi.org/10.5194/nhess-25-1425-2025
120. 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
121. 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
122. Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy) L. Lombardo et al. https://doi.org/10.1007/s11069-015-1915-3
123. 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
124. 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
125. 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
126. Assessment of land subsidence susceptibility in Semnan plain (Iran): a comparison of support vector machine and weights of evidence data mining algorithms M. Mohammady et al. https://doi.org/10.1007/s11069-019-03785-z
127. Introducing intense rainfall and snowmelt variables to implement a process-related non-stationary shallow landslide susceptibility analysis C. Camera et al. https://doi.org/10.1016/j.scitotenv.2021.147360
128. Dependence of debris flow susceptibility maps on sampling strategy with data-driven grid-based model N. Jiang et al. https://doi.org/10.1016/j.ecolind.2024.112534
129. 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
130. Dynamic Landslide Susceptibility Assessment Integrating SBAS-InSAR and Interpretable Machine Learning: A Case Study of the Baihetan Reservoir Area, Southwest China H. Wang et al. https://doi.org/10.3390/rs18040578
131. Estimating global landslide susceptibility and its uncertainty through ensemble modeling A. Felsberg et al. https://doi.org/10.5194/nhess-22-3063-2022
132. 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
133. A comparative study for landslide susceptibility assessment using machine learning algorithms based on grid unit and slope unit N. Deng et al. https://doi.org/10.3389/fenvs.2022.1009433
134. Event-Based Landslide Modeling in the Styrian Basin, Austria: Accounting for Time-Varying Rainfall and Land Cover R. Knevels et al. https://doi.org/10.3390/geosciences10060217
135. 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
136. 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
137. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques W. Chen et al. https://doi.org/10.1016/j.geomorph.2017.09.007
138. 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
139. 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
140. The Effect of Observation Data Sampling Methods on Infiltration Areas by Maximum Entropy Model M. Jafarzadeh et al. https://doi.org/10.52547/jwmr.11.22.96
141. Data-driven analysis of shallow landslide-to-stream connectivity A. Tsyplenkov et al. https://doi.org/10.1016/j.gespch.2025.100002
142. Improving rainfall-triggered landslide susceptibility mapping through source-area boundary sampling and multi- dimensional feature analysis H. Zhao et al. https://doi.org/10.1016/j.gr.2025.11.024
143. Landslide susceptibility assessment in the Anfu County, China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND) H. Hong et al. https://doi.org/10.1007/s12145-018-0352-8
144. 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
145. 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
146. 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
147. A deformation-informed posterior probability updating framework for landslide susceptibility assessment: application to the Motuo area, China M. Hou et al. https://doi.org/10.1016/j.catena.2026.110369
148. Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping Y. Wu et al. https://doi.org/10.1016/j.catena.2019.104396
149. GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India S. Pal & I. Chowdhuri https://doi.org/10.1007/s42452-019-0422-7
150. 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
151. The Role of Historical Data to Investigate Slow-Moving Landslides by Long-Term Monitoring Systems in Lower Austria P. Marr et al. https://doi.org/10.3390/land12030659
152. Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China X. Hu et al. https://doi.org/10.1007/s11069-020-04371-4
153. Comparison of Landslide Susceptibility Maps using Logistic Regression (LR) and Generalized Additive Model (GAM) Z. Timoori Yansari et al. https://doi.org/10.29252/jwmr.9.18.208
154. Assessing the Available Landslide Susceptibility Map and Inventory for the Municipality of Rio de Janeiro, Brazil: Potentials and Challenges for Data-Driven Applications P. Lima et al. https://doi.org/10.3390/ijgi14090330
155. Susceptibility assessment of debris flow hazards from the perspective of watershed units grounded in the Random Forest (RF) model—a case study in the upper reaches of the Fujiang River W. Liu et al. https://doi.org/10.1038/s41598-026-50681-1
156. Comparative Assessment of Three Nonlinear Approaches for Landslide Susceptibility Mapping in a Coal Mine Area Q. Su et al. https://doi.org/10.3390/ijgi6070228
157. Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey M. Ada & B. San https://doi.org/10.1007/s11069-017-3043-8
158. The role of multi-resolution DEMs and sampling strategy uncertainty in deep learning-based debris flow susceptibility mapping H. Daud et al. https://doi.org/10.1007/s11440-025-02841-2
159. Exploring the dynamics of a complex, slow-moving landslide in the Austrian Flysch Zone with 4D surface and subsurface information M. Stumvoll et al. https://doi.org/10.1016/j.catena.2022.106203
160. Slope Unit Maker (SUMak): an efficient and parameter-free algorithm for delineating slope units to improve landslide modeling J. Woodard et al. https://doi.org/10.5194/nhess-24-1-2024
161. Landslide susceptibility mapping with the fusion of multi-feature SVM model based FCM sampling strategy: A case study from Shaanxi Province M. Liu et al. https://doi.org/10.1080/19479832.2021.1961316
162. 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
163. Artificial neural networks applied to landslide susceptibility: The effect of sampling areas on model capacity for generalization and extrapolation S. Gameiro et al. https://doi.org/10.1016/j.apgeog.2021.102598
164. Threshold or Limit? Precipitation Dependency of Austrian Landslides, an Ongoing Challenge for Hazard Mapping under Climate Change I. Offenthaler et al. https://doi.org/10.3390/su12156182
165. Landslide Susceptibility Assessment of Mauritius Island (Indian Ocean) V. Marsala et al. https://doi.org/10.3390/geosciences9120493
166. Landslide susceptibility zonation using GIS and evidential belief function model Y. Wu & Y. Ke https://doi.org/10.1007/s12517-016-2722-1
167. Distinct Susceptibility Patterns of Active and Relict Landslides Reveal Distinct Triggers: A Case in Northwestern Turkey M. Loche et al. https://doi.org/10.3390/rs14061321
168. Automated Machine Learning-Based Landslide Susceptibility Mapping for the Three Gorges Reservoir Area, China J. Ma et al. https://doi.org/10.1007/s11004-023-10116-3
169. 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
170. A benchmark dataset and workflow for landslide susceptibility zonation M. Alvioli et al. https://doi.org/10.1016/j.earscirev.2024.104927
171. Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin Q. Hu et al. https://doi.org/10.1016/j.geomorph.2019.106975
172. Landslide susceptibility, ensemble machine learning, and accuracy methods in the southern Sinai Peninsula, Egypt: Assessment and Mapping A. Youssef et al. https://doi.org/10.1007/s11069-024-06769-w
173. Shallow landslides susceptibility assessment in different environments M. Persichillo et al. https://doi.org/10.1080/19475705.2016.1265011
174. Improving transferability strategies for debris flow susceptibility assessment: Application to the Saponara and Itala catchments (Messina, Italy) M. Cama et al. https://doi.org/10.1016/j.geomorph.2017.03.025
175. Spatially distributed rainfall information and its potential for regional landslide early warning systems E. Canli et al. https://doi.org/10.1007/s11069-017-2953-9
176. Estimation of the susceptibility of a road network to shallow landslides with the integration of the sediment connectivity M. Bordoni et al. https://doi.org/10.5194/nhess-18-1735-2018
177. Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas A. Dornik et al. https://doi.org/10.1038/s41598-022-06257-w
178. Exploring relationships between grid cell size and accuracy for debris-flow susceptibility models: a test in the Giampilieri catchment (Sicily, Italy) M. Cama et al. https://doi.org/10.1007/s12665-015-5047-6
179. A comparative analysis of statistical landslide susceptibility mapping in the southeast region of Minas Gerais state, Brazil C. Barella et al. https://doi.org/10.1007/s10064-018-1341-3
180. Assessing the utility of regionalized rock-mass geomechanical properties in rockfall susceptibility modelling in an alpine environment G. Bajni et al. https://doi.org/10.1016/j.geomorph.2022.108401
181. Determining the terrain characteristics related to the surface expression of subsurface water pressurization in permafrost landscapes using susceptibility modelling J. Holloway et al. https://doi.org/10.5194/tc-11-1403-2017
182. Geographically Weighted Random Forest Considering Spatial Heterogeneity for Landslide Susceptibility Assessment: A Case Study in Yingjiang County W. Qian et al. https://doi.org/10.3390/s26041142
183. Assessing landslide susceptibility and dynamics at cultural heritage sites by integrating machine learning techniques and persistent scatterer interferometry J. Bonini et al. https://doi.org/10.1016/j.geomorph.2024.109522
184. 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
185. Effect of scale and mapping unit on landslide susceptibility mapping of Mandakini River Basin, Uttarakhand, India S. Gupta & D. Shukla https://doi.org/10.1007/s12665-022-10487-6
186. Validation and prediction challenges in data-driven landslide susceptibility mapping: insights from random sampling of training and test data in machine learning models C. Barella et al. https://doi.org/10.1007/s10064-026-04911-5
187. 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
188. Mathematical processing of results of determining geodetic distances between landslides and infrastructure objects in different coordinate systems D. Kasiyanchuk & E. Kuzmenko https://doi.org/10.23939/istcgcap2025.102.025