223 citations as recorded by crossref.

1. Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping Z. Liang et al. https://doi.org/10.1007/s00477-020-01893-y
2. Multi-Scale Engineering Geological Zonation for Linear Projects in Mountainous Regions: A Case Study of National Highway 318 Chengdu-Shigatse Section Y. Li et al. https://doi.org/10.3390/rs15184619
3. Indices of sediment connectivity: opportunities, challenges and limitations T. Heckmann et al. https://doi.org/10.1016/j.earscirev.2018.08.004
4. Soil Electrical Resistivity for Spatial Sampling Design, Prediction, and Uncertainty Modeling of Soil Moisture G. Calamita et al. https://doi.org/10.2136/vzj2017.01.0022
5. Ensemble stacking: a powerful tool for landslide susceptibility assessment – a case study in Anhua County, Hunan Province, China L. Liu et al. https://doi.org/10.1080/10106049.2024.2326005
6. Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran Y. Bahrami et al. https://doi.org/10.1007/s10708-020-10162-y
7. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree D. Tien Bui et al. https://doi.org/10.1007/s10346-015-0557-6
8. Chapter 13 Landslides on São Miguel Island (Azores): susceptibility analysis and validation of rupture zones using a bivariate GIS-based statistical approach R. Marques et al. https://doi.org/10.1144/M44.13
9. A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping Z. Liang et al. https://doi.org/10.3390/rs13081464
10. Acquiring vulnerability indicators to geo-hydrological hazards: An example of mobile phone-based data collection P. Salvati et al. https://doi.org/10.1016/j.ijdrr.2021.102087
11. A comparison of statistical and machine learning methods for debris flow susceptibility mapping Z. Liang et al. https://doi.org/10.1007/s00477-020-01851-8
12. Magnitude–frequency characteristics and preparatory factors for spatial debris-slide distribution in the northern Faroe Islands M. Dahl et al. https://doi.org/10.1016/j.geomorph.2012.09.015
13. Landslide susceptibility assessment using a three-level stacking ensemble strategy Y. Liu et al. https://doi.org/10.1080/10095020.2026.2624287
14. Characterization and quantification of path dependency in landslide susceptibility J. Samia et al. https://doi.org/10.1016/j.geomorph.2017.04.039
15. Temporal prediction modeling for rainfall-induced shallow landslide hazards using extreme value distribution J. Lee et al. https://doi.org/10.1007/s10346-020-01502-7
16. 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
17. The Lasting Signatures of Past Landslides on Soil Stripping From Landscapes L. Fan et al. https://doi.org/10.1029/2021WR030375
18. A comparative study of different machine learning models for landslide susceptibility prediction: a case study of Kullu-to-Rohtang pass transport corridor, India . Nirbhav et al. https://doi.org/10.1007/s12665-023-10846-x
19. Enhancing Landslide Susceptibility Mapping by Integrating Neighboring Information in Slope Units: A Spatial Logistic Regression L. Li et al. https://doi.org/10.3390/rs16234475
20. Debris flow susceptibility mapping using the Rock Engineering System (RES) method: a case study D. Vianello et al. https://doi.org/10.1007/s10346-022-01985-6
21. Multi-Temporal Landslide Inventory-Based Statistical Susceptibility Modeling Associated With the 2017 Mw 6.5 Jiuzhaigou Earthquake, Sichuan, China L. Luo et al. https://doi.org/10.3389/fenvs.2022.858635
22. Assessing disasters in East Kalimantan: machine learning approaches for sustainable urban development S. Heo & D. Lee https://doi.org/10.1088/1748-9326/adf97c
23. A general landslide distribution applied to a small inventory in Todi, Italy D. Turcotte et al. https://doi.org/10.1144/GSL.SP.2006.261.01.08
24. Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model D. Camilo et al. https://doi.org/10.1016/j.envsoft.2017.08.003
25. Shifts in landslide frequency–area distribution after forest conversion in the tropical Andes M. Guns & V. Vanacker https://doi.org/10.1016/j.ancene.2014.08.001
26. Photo-geology of the Montefalco Quaternary Basin, Umbria, Central Italy F. Bucci et al. https://doi.org/10.1080/17445647.2016.1210042
27. 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
28. Morphological and kinematic evolution of a large earthflow: The Montaguto landslide, southern Italy D. Giordan et al. https://doi.org/10.1016/j.geomorph.2012.12.035
29. 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
30. Analysis of historical landslide time series in the Emilia‐Romagna region, northern Italy M. Rossi et al. https://doi.org/10.1002/esp.1858
31. Addressing Missing Data in Slope Displacement Monitoring: Comparative Analysis of Advanced Imputation Methods S. Lee et al. https://doi.org/10.3390/buildings15020236
32. Sensitivity evaluation and prediction of extreme rainfall-induced landslides in Xinning, Hunan, China, using Gumbel extreme value theory and random forest model Y. Yu et al. https://doi.org/10.1007/s12665-026-12953-x
33. GIS-based landslide susceptibility mapping using bivariate statistical analysis in Devrek (Zonguldak-Turkey) C. Yilmaz et al. https://doi.org/10.1007/s12665-011-1196-4
34. Dynamic path-dependent landslide susceptibility modelling J. Samia et al. https://doi.org/10.5194/nhess-20-271-2020
35. Recommendations for the quantitative analysis of landslide risk J. Corominas et al. https://doi.org/10.1007/s10064-013-0538-8
36. 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
37. Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China Z. Chen et al. https://doi.org/10.1080/10106049.2017.1404143
38. Landslide susceptibility assessment in Tongguan District Anhui China using information value and certainty factor models D. Ding et al. https://doi.org/10.1038/s41598-025-93704-z
39. Advances in landslide nowcasting: evaluation of a global and regional modeling approach D. Kirschbaum et al. https://doi.org/10.1007/s12665-011-0990-3
40. An Event-Based Inventory Approach in Landslide Hazard Assessment: The Case of the Skolis Mountain, Northwest Peloponnese, Greece A. Litoseliti et al. https://doi.org/10.3390/ijgi9070457
41. 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
42. Comparing physical and statistical landslide susceptibility models at the scale of individual trees R. Spiekermann et al. https://doi.org/10.1016/j.geomorph.2023.108870
43. LAND-deFeND – An innovative database structure for landslides and floods and their consequences E. Napolitano et al. https://doi.org/10.1016/j.jenvman.2017.11.022
44. The influence of land use and land cover change on landslide susceptibility: a case study in Zhushan Town, Xuan'en County (Hubei, China) L. Chen et al. https://doi.org/10.5194/nhess-19-2207-2019
45. Precipitation data and their uncertainty as input for rainfall-induced shallow landslide models Y. Chen et al. https://doi.org/10.1007/s11707-019-0791-7
46. Activity maps of multi-source mudslides from the Daunia Apennines (Apulia, southern Italy) L. Spalluto et al. https://doi.org/10.1007/s11069-020-04461-3
47. A Landslide Probability Model Based on a Long-Term Landslide Inventory and Rainfall Factors C. Wu & Y. Yeh https://doi.org/10.3390/w12040937
48. Economic landslide susceptibility under a socio-economic perspective: an application to Umbria Region (Central Italy) M. Donnini et al. https://doi.org/10.1007/s10037-020-00143-6
49. Comparative analysis of slope and grid units for co-seismic landslide susceptibility mapping using machine learning methods T. Bhattarai & N. Bhandary https://doi.org/10.1007/s44288-026-00489-3
50. Rainfall-conditioned landslide susceptibility mapping using an FR–CART hybrid framework in southern Thailand T. Pornbunyanon et al. https://doi.org/10.1016/j.pdisas.2026.100530
51. GIS-Multicriteria Analysis Using AHP to Evaluate the Landslide Risk in Road Lifelines G. Leonardi et al. https://doi.org/10.3390/app12094707
52. A Residual Neural Network Integrated with a Hydrological Model for Global Flood Susceptibility Mapping Based on Remote Sensing Datasets J. Liu et al. https://doi.org/10.3390/rs15092447
53. Relevance of tectonic and structural parameters in Triassic bedrock formations to landslide susceptibility in Quaternary hillslope sediments B. Damm et al. https://doi.org/10.1016/j.quaint.2010.02.022
54. Comparative assessment of landslide susceptibility. Case study: the Niraj river basin (Transylvania depression, Romania) R. Sanda et al. https://doi.org/10.1080/19475705.2015.1030784
55. Is it possible to quantify pebble abrasion and velocity in rivers using terrestrial cosmogenic nuclides? S. Carretier & V. Regard https://doi.org/10.1029/2011JF001968
56. Landslide hazard assessment: recent trends and techniques S. Pardeshi et al. https://doi.org/10.1186/2193-1801-2-523
57. Comparative assessment of landslide susceptibility using heuristic and statistical models in Chamoli, Uttarakhand, India S. Kunwar et al. https://doi.org/10.1186/s40703-026-00287-3
58. A nation-wide system for landslide mapping and risk management in Italy: The second Not-ordinary Plan of Environmental Remote Sensing D. Di Martire et al. https://doi.org/10.1016/j.jag.2017.07.018
59. Integrated research on disaster risk: Is it really integrated? M. Gall et al. https://doi.org/10.1016/j.ijdrr.2015.01.010
60. Landslide susceptibility analyses using Random Forest, C4.5, and C5.0 with balanced and unbalanced datasets B. Tanyu et al. https://doi.org/10.1016/j.catena.2021.105355
61. Landslide susceptibility map refinement using PSInSAR data A. Ciampalini et al. https://doi.org/10.1016/j.rse.2016.07.018
62. Combining Satellite InSAR, Slope Units and Finite Element Modeling for Stability Analysis in Mining Waste Disposal Areas J. López-Vinielles et al. https://doi.org/10.3390/rs13102008
63. Landslide potential mapping applying maximum entropy to continuous change maps R. Ramos-Bernal et al. https://doi.org/10.1007/s12518-024-00596-1
64. Landslide hazard assessment and mapping at national scale for Malawi Y. Thiery et al. https://doi.org/10.1016/j.jafrearsci.2024.105187
65. Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model Z. Chen et al. https://doi.org/10.1007/s12665-021-09603-9
66. 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
67. A method for the assessment of the influence of bedding on landslide abundance and types M. Santangelo et al. https://doi.org/10.1007/s10346-014-0485-x
68. Quantifying the influence of individual trees on slope stability at landscape scale R. Spiekermann et al. https://doi.org/10.1016/j.jenvman.2021.112194
69. Implementing landslide path dependency in landslide susceptibility modelling J. Samia et al. https://doi.org/10.1007/s10346-018-1024-y
70. Reducing subjectivity in landslide susceptibility mapping method: an integrated methodology S. Gebru & T. Shewangzaw https://doi.org/10.1186/s40677-026-00365-9
71. 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
72. The future of landslides’ past—a framework for assessing consecutive landsliding systems A. Temme et al. https://doi.org/10.1007/s10346-020-01405-7
73. Slope stability assessment of a major trunk road at Scrabster Harbour, Scotland, UK M. Katchkin et al. https://doi.org/10.1144/qjegh2021-187
74. Bridging Gaps in Landslide Mapping: A Semi-Quantitative Empirical Framework for Delineating Key Areas to Improve Collection of Essential Field-Based and Supplementary Remote-Based Data N. Perilli et al. https://doi.org/10.3390/infrastructures11010011
75. Landslide susceptibility zonation using geospatial technique and analytical hierarchy process in Sikkim Himalaya I. Sonker et al. https://doi.org/10.1016/j.qsa.2021.100039
76. 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
77. Landslide characterization and distribution in northern Ethiopia: a case study from the Adishu area K. Amare et al. https://doi.org/10.1007/s10064-017-1064-x
78. Landslide susceptibility assessment in Zhenxiong County of China based on geographically weighted logistic regression model T. Gu et al. https://doi.org/10.1080/10106049.2021.1903571
79. Assessing the effectiveness of alternative landslide partitioning in machine learning methods for landslide prediction in the complex Himalayan terrain M. Riaz et al. https://doi.org/10.1177/03091333221113660
80. The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution A. Rosi et al. https://doi.org/10.1007/s10346-017-0861-4
81. Shallow landslides susceptibility assessment in different environments M. Persichillo et al. https://doi.org/10.1080/19475705.2016.1265011
82. The multi-temporal landslide inventory map of the Collazzone study area, central Italy F. Ardizzone et al. https://doi.org/10.5194/essd-18-5545-2026
83. Solving large deformation problems in geotechnical and geo-environmental engineering with the smoothed particle hydrodynamics: a state-of-the-art review of constitutive solutions K. Onyelowe et al. https://doi.org/10.1007/s12665-023-11079-8
84. Combining spatial modelling and regionalization of rainfall thresholds for debris flows hazard mapping in the Emilia-Romagna Apennines (Italy) C. G. et al. https://doi.org/10.1007/s10346-021-01739-w
85. A holistic approach of remote sensing, GIS, and machine learning for shallow landslide susceptibility mapping in Gaganbawada region of Western Ghats, India A. Patil & S. Panhalkar https://doi.org/10.1007/s43538-024-00305-x
86. Preparing first-time slope failures hazard maps: from pixel-based to slope unit-based G. Domènech et al. https://doi.org/10.1007/s10346-019-01279-4
87. Climate change-induced shifts in landslide susceptibility in São Sebastião (southeastern Brazil) E. Alcântara et al. https://doi.org/10.1016/j.nhres.2024.11.005
88. How can statistical models help to determine driving factors of landslides? P. Vorpahl et al. https://doi.org/10.1016/j.ecolmodel.2011.12.007
89. Characterising the spatial distribution, frequency and geomorphic controls on landslide occurrence, Molise, Italy E. Borgomeo et al. https://doi.org/10.1016/j.geomorph.2014.08.004
90. Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis H. Kim et al. https://doi.org/10.1016/j.enggeo.2021.106372
91. Estimating the quality of landslide susceptibility models F. Guzzetti et al. https://doi.org/10.1016/j.geomorph.2006.04.007
92. Landslides in a changing climate S. Gariano & F. Guzzetti https://doi.org/10.1016/j.earscirev.2016.08.011
93. 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
94. Landslide hazard probability and risk assessment at the community level: a case of western Hubei, China S. Fu et al. https://doi.org/10.5194/nhess-20-581-2020
95. Non-susceptible landslide areas in Italy and in the Mediterranean region I. Marchesini et al. https://doi.org/10.5194/nhess-14-2215-2014
96. Enhancing landslide susceptibility mapping incorporating landslide typology via stacking ensemble machine learning in Three Gorges Reservoir, China L. Yu et al. https://doi.org/10.1016/j.gsf.2024.101802
97. Stacking ensemble of machine learning methods for landslide susceptibility mapping in Zhangjiajie City, Hunan Province, China Y. Huan et al. https://doi.org/10.1007/s12665-022-10723-z
98. The contribution of satellite SAR-derived displacement measurements in landslide risk management practices F. Raspini et al. https://doi.org/10.1007/s11069-016-2691-4
99. Comparing landslide inventory maps M. Galli et al. https://doi.org/10.1016/j.geomorph.2006.09.023
100. Regional Landslide Hazard Assessment Using Extreme Value Analysis and a Probabilistic Physically Based Approach H. Park et al. https://doi.org/10.3390/su14052628
101. 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
102. Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images A. Mondini et al. https://doi.org/10.1016/j.rse.2011.03.006
103. Chrono-validation of near-real-time landslide susceptibility models via plug-in statistical simulations L. Lombardo & H. Tanyas https://doi.org/10.1016/j.enggeo.2020.105818
104. A ground instabilities inventory between Tetouan and Jebha (Morocco): mapping, description and analysis H. Tribak et al. https://doi.org/10.1007/s10064-023-03161-z
105. Landslide mapping with multi-scale object-based image analysis – a case study in the Baichi watershed, Taiwan T. Lahousse et al. https://doi.org/10.5194/nhess-11-2715-2011
106. Quantitative landslide risk analysis in the Hashtchin area (NW-Iran) R. Talaei & S. Samadov https://doi.org/10.1080/19648189.2016.1229224
107. The Influence of Different Knowledge-Driven Methods on Landslide Susceptibility Mapping: A Case Study in the Changbai Mountain Area, Northeast China Z. Ma et al. https://doi.org/10.3390/e21040372
108. Assessment of landslide age, landslide persistence and human impact using airborne laser scanning digital terrain models R. Bell et al. https://doi.org/10.1111/j.1468-0459.2012.00454.x
109. Qualitative Rockfall Hazard Assessment: A Comprehensive Review of Current Practices F. Ferrari et al. https://doi.org/10.1007/s00603-016-0918-z
110. Earthquake-triggered landslide susceptibility in Italy by means of Artificial Neural Network G. Amato et al. https://doi.org/10.1007/s10064-023-03163-x
111. Comprehensive risk mapping and management of rainfall-induced slope failures in urbanizing subtropical regions F. Yang & M. Hasan https://doi.org/10.1038/s41598-026-49114-w
112. Mapping landslide susceptibility using data-driven methods J. Zêzere et al. https://doi.org/10.1016/j.scitotenv.2017.02.188
113. Assessing Many Image Processing Products Retrieved from Sentinel-2 Data to Monitor Shallow Landslides in Agricultural Environments R. Cavalli et al. https://doi.org/10.3390/rs16132286
114. Lithological and seasonal control on rainfall thresholds for the possible initiation of landslides in central Italy S. Peruccacci et al. https://doi.org/10.1016/j.geomorph.2011.10.005
115. Variations in the susceptibility to landslides, as a consequence of land cover changes: A look to the past, and another towards the future L. Pisano et al. https://doi.org/10.1016/j.scitotenv.2017.05.231
116. Satellite interferometric data for landslide intensity evaluation in mountainous regions L. Solari et al. https://doi.org/10.1016/j.jag.2019.102028
117. Landslide susceptibility evaluation and hazard zonation techniques – a review L. Shano et al. https://doi.org/10.1186/s40677-020-00152-0
118. 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
119. Automatic Identification Model for Landslide Disaster Using Remote Sensing Images Based on Improved Multiresunet Z. Zhao et al. https://doi.org/10.1109/ACCESS.2024.3525067
120. Landslide susceptibility assessment using the frequency ratio model in the Mae Chan River watershed, northern Thailand P. Manopkawee & N. Mankhemthong https://doi.org/10.1016/j.qsa.2024.100263
121. Multi-Model Geostatistical and Machine Learning-Based Framework for Landslide Susceptibility and Risk Assessment in the Upper Ravi Basin, North-Western Himalaya, India R. Kumar et al. https://doi.org/10.1007/s41748-026-01229-w
122. Heyelan Duyarlılığının Analitik Hiyerarşi Süreci (AHS) ve Frekans Oranı (FR) ile Belirlenmesi: Kürk Çayı Havzası (Elazığ) Örneği T. Öz https://doi.org/10.18069/firatsbed.1572408
123. Probabilistic landslide hazard and indicator-based risk assessment in a data-scarce Indian Himalayan terrain S. Das et al. https://doi.org/10.1007/s10064-024-03861-0
124. Study on landslide susceptibility mapping with different factor screening methods and random forest models T. Gu et al. https://doi.org/10.1371/journal.pone.0292897
125. Landslide failure and runout susceptibility in the upper T. Ceno valley (Northern Apennines, Italy) A. Clerici et al. https://doi.org/10.1007/s11069-009-9349-4
126. Preliminary assessment of landslide vulnerability of road-cut slopes along the Garhwal Himalayas, using landslide possibility index (LPI) approach S. Singh et al. https://doi.org/10.1007/s10751-026-02396-y
127. Evaluating the quality of landslide inventory maps: comparison between archive and surveyed inventories for the Daunia region (Apulia, Southern Italy) R. Pellicani & G. Spilotro https://doi.org/10.1007/s10064-014-0639-z
128. A review of statistically-based landslide susceptibility models P. Reichenbach et al. https://doi.org/10.1016/j.earscirev.2018.03.001
129. Landslide Vulnerability and Risk Assessment of Matara District Using the Geospatial Technologies P. Kokawalage & H. Herath https://doi.org/10.64211/oidaijsd190506
130. A Heuristic Method to Evaluate the Effect of Soil Tillage on Slope Stability: A Pilot Case in Central Italy E. Volpe et al. https://doi.org/10.3390/land11060912
131. Uncertainty in landslide occurrence prediction using Dempster–Shafer theory A. Milaghardan et al. https://doi.org/10.1007/s40808-016-0240-5
132. Landslide susceptibility zoning through physically-based limit equilibrium method modelling F. Mevoli et al. https://doi.org/10.1016/j.catena.2025.109726
133. Integrating spatial, temporal, and size probabilities for the annual landslide hazard maps in the Shihmen watershed, Taiwan C. Wu & S. Chen https://doi.org/10.5194/nhess-13-2353-2013
134. Logistic regression applied to natural hazards: rare event logistic regression with replications M. Guns & V. Vanacker https://doi.org/10.5194/nhess-12-1937-2012
135. Integrated Analysis of Rockfalls and Floods in the Jiului Gorge, Romania: Impacts on Road and Rail Traffic M. Puie & B. Mihai https://doi.org/10.3390/app142210270
136. Occurrence of landsliding on slopes where flowsliding had previously occurred: An investigation in a loess platform, North-west China L. Xu et al. https://doi.org/10.1016/j.catena.2012.11.010
137. Landslides and vegetation fragmentation drive hydro-sedimentary hazards and urban flooding in Uvira P. Tomombwa Kumbusa et al. https://doi.org/10.1007/s44274-026-00987-8
138. Spatially Explicit Erosion Severity as a Proxy for Landslide Susceptibility in a Mountainous Watershed S. Stefanidis et al. https://doi.org/10.3390/app16157477
139. From static to dynamic landslide susceptibility: a multi-temporal inventory-based approach in the Belluno Alps (NE Italy) R. Bhookya et al. https://doi.org/10.1007/s12665-026-13064-3
140. 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
141. Landslide susceptibility mapping using GIS Matrix Method and Frequency Ratio, application in the marly context of Moulay Yacoub Region, Morocco I. Obda et al. https://doi.org/10.1051/bsgf/2023016
142. An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland E. Bryce et al. https://doi.org/10.1007/s10346-024-02368-9
143. Landslide Susceptibility Mapping in Terms of the Slope-Unit or Raster-Unit, Which is Better? S. Ma et al. https://doi.org/10.1007/s12583-021-1407-1
144. Improving predictive power of physically based rainfall-induced shallow landslide models: a probabilistic approach S. Raia et al. https://doi.org/10.5194/gmd-7-495-2014
145. A strategy for GIS-based 3-D slope stability modelling over large areas M. Mergili et al. https://doi.org/10.5194/gmd-7-2969-2014
146. 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
147. AHP-based landslide susceptibility assessment along National highway 7 (651.000 KM to 731.000 KM) in India G. Kumar et al. https://doi.org/10.1007/s43538-025-00519-7
148. Spatially distributed three-dimensional slope stability modelling in a raster GIS M. Mergili et al. https://doi.org/10.1016/j.geomorph.2013.10.008
149. Lineament mapping and its application in landslide hazard assessment: a review M. Ramli et al. https://doi.org/10.1007/s10064-009-0255-5
150. Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment G. Amato et al. https://doi.org/10.1016/j.enggeo.2019.105237
151. From scenario-based seismic hazard to scenario-based landslide hazard: rewinding to the past via statistical simulations L. Luo et al. https://doi.org/10.1007/s00477-020-01959-x
152. Landslide consequence analysis: a region-scale indicator-based methodology A. Puissant et al. https://doi.org/10.1007/s10346-013-0429-x
153. Incorporating spatial heterogeneity of environmental variables into landslide susceptibility prediction X. Zhao et al. https://doi.org/10.1080/10106049.2025.2603057
154. Landslide Hazard Assessment for National Rail Network K. Freeborough et al. https://doi.org/10.1016/j.proeng.2016.06.104
155. Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India D. Dhakal et al. https://doi.org/10.1038/s41598-025-95087-7
156. Optimal landslide susceptibility zonation based on multiple forecasts M. Rossi et al. https://doi.org/10.1016/j.geomorph.2009.06.020
157. Bayesian framework for mapping and classifying shallow landslides exploiting remote sensing and topographic data A. Mondini et al. https://doi.org/10.1016/j.geomorph.2013.06.015
158. Landslide susceptibility assessment using different slope units based on the evidential belief function model Z. Chen et al. https://doi.org/10.1080/10106049.2019.1582716
159. Landslides in Tijuana, Mexico: hazard assessment in an urban neighborhood A. Oliva González et al. https://doi.org/10.14483/22487638.17882
160. 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
161. Scale Effects in Landslide Susceptibility Assessment: Integrating Slope Unit Division and SHAP-Based Interpretability in a Typical River Basin W. Hu et al. https://doi.org/10.3390/w17131877
162. A review on spatial, temporal and magnitude prediction of landslide hazard A. Tyagi et al. https://doi.org/10.1016/j.jaesx.2022.100099
163. Exploring the potential relationship between the occurrence of debris flow and landslides Z. Liang et al. https://doi.org/10.5194/nhess-21-1247-2021
164. Physically-based modelling of granular flows with Open Source GIS M. Mergili et al. https://doi.org/10.5194/nhess-12-187-2012
165. Predicting Dynamic Landslide Susceptibility Under Changing Land-Use Scenarios with Generalized Additive Model H. Xie et al. https://doi.org/10.3390/su18168371
166. Landslide inventory maps: New tools for an old problem F. Guzzetti et al. https://doi.org/10.1016/j.earscirev.2012.02.001
167. Landslide susceptibility assessment: what are the effects of mapping unit and mapping method? A. Erener & H. Düzgün https://doi.org/10.1007/s12665-011-1297-0
168. Hazard and population vulnerability analysis: a step towards landslide risk assessment F. Murillo-García et al. https://doi.org/10.1007/s11629-016-4179-9
169. Evaluation of Unsupervised Change Detection Methods Applied to Landslide Inventory Mapping Using ASTER Imagery R. Ramos-Bernal et al. https://doi.org/10.3390/rs10121987
170. The role of geomorphology, rainfall and soil moisture in the occurrence of landslides triggered by 2018 Typhoon Mangkhut in the Philippines C. Abancó et al. https://doi.org/10.5194/nhess-21-1531-2021
171. Characterizing Rainfall Discrepancies Between Landslide Sites and the Nearest Rain Gauges Using Radar Estimates: A Case Study from Italy C. Vennari et al. https://doi.org/10.3390/rs18091435
172. A national assessment of landslide hazard from Outside Party Slopes to the rail network of Great Britain K. Freeborough et al. https://doi.org/10.1144/qjegh2018-029
173. 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
174. Shallow-landslide stability evaluation in loess areas according to the Revised Infinite Slope Model: a case study of the 7.25 Tianshui sliding-flow landslide events of 2013 in the southwest of the Loess Plateau, China J. Zhuang et al. https://doi.org/10.5194/nhess-24-2615-2024
175. A Hybrid Variable Weight Theory Approach of Hierarchical Analysis and Multi-Layer Perceptron for Landslide Susceptibility Evaluation: A Case Study in Luanchuan County, China M. Li et al. https://doi.org/10.3390/su15031908
176. Causes of landslide recurrence in a loess platform with respect to hydrological processes L. Xu et al. https://doi.org/10.1007/s11069-012-0326-y
177. Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras G. Suárez & M. Domínguez-Cuesta https://doi.org/10.3390/app14199114
178. Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides L. Lombardo et al. https://doi.org/10.1029/2019JF005056
179. Landslide susceptibility mapping along Rishikesh–Badrinath national highway (Uttarakhand) by applying multi-criteria decision-making (MCDM) approach M. Ramiz et al. https://doi.org/10.1007/s12665-023-11268-5
180. Data Fusion Technique Using Wavelet Transform and Taguchi Methods for Automatic Landslide Detection From Airborne Laser Scanning Data and QuickBird Satellite Imagery B. Pradhan et al. https://doi.org/10.1109/TGRS.2015.2484325
181. Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey) A. Akgun et al. https://doi.org/10.1007/s10661-011-2352-8
182. 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
183. Topographic predictors of susceptibility to alluvial fan flooding, Southern Apennines N. Santangelo et al. https://doi.org/10.1002/esp.3197
184. Economic Risk Assessment of Future Debris Flows by Machine Learning Method C. Qiu et al. https://doi.org/10.1007/s13753-024-00545-x
185. GIS and Machine Learning Framework for Landslide Susceptibility Mapping: A Raster-Based Approach A. Iradukunda & A. Özgün https://doi.org/10.48123/rsgis.1864203
186. Application of environmental variables in statistically-based landslide susceptibility mapping: A review X. Zhao et al. https://doi.org/10.3389/feart.2023.1147427
187. Shaping Sustainable Urban Environments by Addressing the Hydro-Meteorological Factors in Landslide Occurrence: Ciuperca Hill (Oradea, Romania) C. Morar et al. https://doi.org/10.3390/ijerph18095022
188. A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units S. Zhao et al. https://doi.org/10.1155/2021/8854606
189. Improved Shallow Landslide Susceptibility Prediction Based on Statistics and Ensemble Learning Z. Liang et al. https://doi.org/10.3390/su14106110
190. Stacking ensemble for improved landslide susceptibility mapping in Darjeeling Himalayas, India S. Kumar et al. https://doi.org/10.1007/s12040-026-02752-0
191. Landslide susceptibility mapping in Lesalso (Laelay Maichew), Northern Ethiopia: a GIS approach using frequency ratio and analytical hierarchy process methods A. Gebrehiwot et al. https://doi.org/10.1007/s40808-025-02578-7
192. Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR) W. Abdulwahid & B. Pradhan https://doi.org/10.1007/s10346-016-0744-0
193. Landslide databases for climate change detection and attribution J. Wood et al. https://doi.org/10.1016/j.geomorph.2020.107061
194. Space-time landslide predictive modelling L. Lombardo et al. https://doi.org/10.1016/j.earscirev.2020.103318
195. 机器学习在滑坡智能防灾减灾中的应用与发展趋势 J. Dou et al. https://doi.org/10.3799/dqkx.2022.419
196. National and regional-scale landslide indicators and indexes: Applications in Italy M. Donnini et al. https://doi.org/10.1515/geo-2022-0375
197. A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China W. Chen et al. https://doi.org/10.1080/19475705.2017.1401560
198. Tree-ring reconstruction of reactivation phases of the Schimbrig landslide (Swiss Alps) J. Lopez-Saez et al. https://doi.org/10.4000/geomorphologie.11825
199. Landslides hazard, vulnerability and risk mapping in the data-poor region of northern Pakistan Y. Ullah et al. https://doi.org/10.1007/s12665-024-11858-x
200. 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
201. Landslide quantitative risk analysis of buildings at the municipal scale based on a rainfall triggering scenario S. Pereira et al. https://doi.org/10.1080/19475705.2016.1250116
202. Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey H. Nefeslioglu et al. https://doi.org/10.1155/2010/901095
203. Landslide size matters: A new data-driven, spatial prototype L. Lombardo et al. https://doi.org/10.1016/j.enggeo.2021.106288
204. A Multi-Criteria GIS-Based Approach for Risk Assessment of Slope Instability Driven by Glacier Melting in the Alpine Area G. Castellazzi & M. Previtali https://doi.org/10.3390/app142411524
205. Residual mechanisms and kinematics of the relict Lemeglio coastal landslide (Liguria, northwestern Italy) A. Cevasco et al. https://doi.org/10.1016/j.geomorph.2018.08.010
206. Evaluation of Conditioning Factors of Slope Instability and Continuous Change Maps in the Generation of Landslide Inventory Maps Using Machine Learning (ML) Algorithms R. Ramos-Bernal et al. https://doi.org/10.3390/rs13224515
207. A CONCEPTUAL STUDY ON METHODOLOGIES FOR LANDSLIDE HAZARD: INVENTORIES, SUSCEPTIBILITY, VULNERABILITY, AND RISK ANALYSIS A. Taye & R. Tamang https://doi.org/10.29121/granthaalayah.v13.i4.2025.6139
208. GIS-based landslide susceptibility mapping method and Shannon entropy model: a case study on Sakaleshapur Taluk, Western Ghats, Karnataka, India A. Rahaman et al. https://doi.org/10.1007/s12517-021-08422-3
209. Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives M. Scaioni et al. https://doi.org/10.3390/rs6109600
210. GIS-based assessment of risk due to salt mining activities at Tuzla (Bosnia and Herzegovina) F. Mancini et al. https://doi.org/10.1016/j.enggeo.2009.06.018
211. Comparison of conditioning factor classification criteria in large-scale statistically based landslide susceptibility models M. Sinčić et al. https://doi.org/10.5194/nhess-25-183-2025
212. A high-precision catalogue of landslide events in China based on news text mining with large language model B. Zhao et al. https://doi.org/10.1038/s41597-026-07066-w
213. A revised landslide inventory of the Campania region (Italy) F. Fusco et al. https://doi.org/10.1038/s41597-023-02155-6
214. Analysis of landslide inventories for accurate prediction of debris-flow source areas J. Blahut et al. https://doi.org/10.1016/j.geomorph.2010.02.017
215. Extreme gradient boosting with Shapley Additive Explanations for landslide susceptibility at slope unit and hydrological response unit scales A. Pradhan et al. https://doi.org/10.1016/j.gsf.2025.102081
216. Temporal correlations and clustering of landslides A. Witt et al. https://doi.org/10.1002/esp.1998
217. GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China Y. Zhao et al. https://doi.org/10.1016/j.enggeo.2019.105147
218. The Influence of Land Use Change on Landslide Susceptibility Zonation: The Briga Catchment Test Site (Messina, Italy) P. Reichenbach et al. https://doi.org/10.1007/s00267-014-0357-0
219. Landslide Vulnerability Criteria: A Case Study from Umbria, Central Italy M. Galli & F. Guzzetti https://doi.org/10.1007/s00267-006-0325-4
220. High-Resolution Lidar-Derived DEM for Landslide Susceptibility Assessment Using AHP and Fuzzy Logic in Serdang, Malaysia J. Okoli et al. https://doi.org/10.3390/geosciences13020034
221. Estimate earth fissure hazard based on machine learning in the Qa’ Jahran Basin, Yemen Y. Al-Masnay et al. https://doi.org/10.1038/s41598-022-26526-y
222. Numerical and analytical models of cosmogenic radionuclide dynamics in landslide‐dominated drainage basins B. Yanites et al. https://doi.org/10.1029/2008JF001088
223. Assessment of data availability for the development of landslide fatality curves T. Grahn & H. Jaldell https://doi.org/10.1007/s10346-016-0775-6