237 citations as recorded by crossref.

1. Long-Term Monitoring of Landslide Activity in a Debris Flow Gully Using SBAS-InSAR: A Case Study of Shawan Gully, China J. Zhang et al. https://doi.org/10.3390/rs17091580
2. Contributions of climate and population change to global flood–landslide multi-hazard population exposure under future climate scenarios X. Wang et al. https://doi.org/10.1016/j.gsf.2026.102363
3. Cultural-ecological integrated governance: Quantifying approaches to reducing landslide risk in traditional Tibetan villages of Western Sichuan, China Q. Liu et al. https://doi.org/10.1007/s11629-025-9832-8
4. A new landslide detection approach using the combination of the normalized difference moisture index and slope classification analysis M. Rendana et al. https://doi.org/10.1080/24749508.2024.2429205
5. Invited perspectives: Landslide populations – can they be predicted? F. Guzzetti https://doi.org/10.5194/nhess-21-1467-2021
6. Centrifuge and numerical modeling of h-type anti-slide pile reinforced soil-rock mixture slope H. Zhang et al. https://doi.org/10.1007/s11629-022-7446-y
7. Deadly Myanmar mine disaster caused by poor planning, say data sleuths A. Silver https://doi.org/10.1038/d41586-021-01740-2
8. Indice-Based Characterization of Precipitation Variability Using Multiple Trend Analysis Methods in the High-Rainfall Eastern Black Sea Basin, Türkiye C. Koycegiz et al. https://doi.org/10.1007/s00024-026-04035-7
9. The role of non-government stakeholders in implementing the Sendai Framework: A view from the voluntary commitments online platform Y. Matsuoka & E. Gonzales Rocha https://doi.org/10.1016/j.pdisas.2021.100142
10. Integrating a hybrid machine learning model with climate projections for landslide susceptibility assessment M. Tumon et al. https://doi.org/10.1016/j.rineng.2025.108067
11. Impact of root diameter, length, and moisture content on the mechanical properties of root systems and the associated implications for ecological slope protection L. Xue et al. https://doi.org/10.1016/j.catena.2025.109634
12. 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
13. Experimental Study on the Relationship between the Velocity of Surface Movements and Tilting Rate in Pre-Failure Stage of Rainfall-Induced Landslides J. Xie et al. https://doi.org/10.3390/s21185988
14. Shared Blocks-Based Ensemble Deep Learning for Shallow Landslide Susceptibility Mapping T. Kavzoglu et al. https://doi.org/10.3390/rs13234776
15. Mapping Landslide Susceptibility Using Machine Learning Algorithms and GIS: A Case Study in Shexian County, Anhui Province, China Z. Wang et al. https://doi.org/10.3390/sym12121954
16. Aspects of different growth responses of coniferous and broadleaved tree species in landslide research K. Šilhán https://doi.org/10.1016/j.dendro.2025.126368
17. Estimating Rainfall Thresholds for Landslide Occurrence in the Bhutan Himalayas A. Dikshit et al. https://doi.org/10.3390/w11081616
18. 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
19. Evaluación de la cinemática del deslizamiento de Cuenca en los Andes de Perú central mediante técnicas fotogramétricas y geodésicas W. Quiroz et al. https://doi.org/10.4995/raet.2024.21785
20. Performance of logistic regression and support vector machine conjunction with the GIS and RS in the landslide susceptibility assessment: Case study in Nakhon Si Thammarat, southern Thailand K. Prathom & C. Sujitapan https://doi.org/10.1016/j.jksus.2024.103306
21. An optimized non-landslide sampling method for Landslide susceptibility evaluation using machine learning models S. Xu et al. https://doi.org/10.1007/s11069-024-07021-1
22. Towards a web service for rapid landslide mapping based on Copernicus data F. Albrecht et al. https://doi.org/10.1088/1755-1315/509/1/012002
23. Framework for rainfall-triggered landslide-prone critical infrastructure zonation K. Gnyawali et al. https://doi.org/10.1016/j.scitotenv.2023.162242
24. Utilization of google maps for depicting landslide pattern in Indonesia S. Sukristiyanti et al. https://doi.org/10.1088/1755-1315/500/1/012042
25. Landslide Characterization Applying Sentinel-1 Images and InSAR Technique: The Muyubao Landslide in the Three Gorges Reservoir Area, China C. Zhou et al. https://doi.org/10.3390/rs12203385
26. Investigation of the dynamic process of the Xinmo landslide using the discrete element method Z. Liu et al. https://doi.org/10.1016/j.compgeo.2020.103561
27. Methodologies for functional recovery assessment of hilly roads situated in the Indian Himalayan region R. Kumar et al. https://doi.org/10.1016/j.iintel.2025.100181
28. Landslide Displacement Prediction Based on the VMD–TCN Hybrid Model C. Xu et al. https://doi.org/10.1007/s11204-025-10057-5
29. Study on Numerical Model and Dynamic Response of Ring Net in Flexible Rockfall Barriers C. Xia et al. https://doi.org/10.3390/su14084406
30. Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India A. Saha et al. https://doi.org/10.3390/land11101711
31. Dynamics and thermodynamics of extreme rainfall event over different subregions of Himachal Pradesh during 8–10 July, 2023 P. Ancy et al. https://doi.org/10.1007/s00703-026-01136-9
32. Study on Optimal Pile Length in Load Section of Double-Row Buried Pile under Overtopping Failure Mode X. Li et al. https://doi.org/10.3390/app14010438
33. Triggering mechanics and early warning for snowmelt-rainfall-induced loess landslide K. Wulamu et al. https://doi.org/10.1038/s41598-024-78309-2
34. Characteristics of failure area and failure mechanism of a landslide in Yingjiang County, Yunnan, China G. Shi et al. https://doi.org/10.1007/s10346-020-01544-x
35. Landslide detection in the Himalayas using machine learning algorithms and U-Net S. Meena et al. https://doi.org/10.1007/s10346-022-01861-3
36. An Increasing Trend of Landslides as a Consequence of the Global Change J. Remondo et al. https://doi.org/10.1007/s41748-025-00685-0
37. Remote Sensing-Based Detection and Statistical Characterization of Large Landslides in the Transylvanian Basin G. Roșian et al. https://doi.org/10.3390/rs18162645
38. The damability function: a probabilistic approach to regional landslide dam susceptibility analysis applied to the Oregon Coast Range, USA P. Morgan et al. https://doi.org/10.5194/nhess-26-1745-2026
39. A deep learning-based model for endorsing predictive accuracies of landslide prediction: insights into soil moisture dynamics K. Ebrahim et al. https://doi.org/10.1186/s40677-025-00333-9
40. Hydrometeorological Control Sampling for Rainfall Induced Landslide Susceptibility Modelling Using Multi Source Data and Advanced Learning F. Zhang et al. https://doi.org/10.3390/atmos17080771
41. Increasing landslide susceptibility and intensity under climate change for Aotearoa New Zealand L. Dreyer et al. https://doi.org/10.1038/s41598-026-46684-7
42. Exploring performance and robustness of shallow landslide susceptibility modeling at regional scale using different training and testing sets M. Conforti et al. https://doi.org/10.1007/s12665-023-10844-z
43. Comparison between mechanical and hydrological reinforcement effects of cultivated plants on shallow slope stability M. Bordoni et al. https://doi.org/10.1016/j.scitotenv.2023.168999
44. Effect of spatial resolution and data splitting on landslide susceptibility mapping using different machine learning algorithms M. Abraham et al. https://doi.org/10.1080/19475705.2021.2011791
45. MiMapper: A Cloud-Based Multi-Hazard Mapping Tool for Nepal C. Price et al. https://doi.org/10.3390/geohazards6040063
46. Divergent patterns of landslide activity and triggering factors at a local scale of a single mountain massif (Island Beskid Mts., Western Carpathians, Poland) M. Wistuba et al. https://doi.org/10.1016/j.enggeo.2024.107531
47. GIS-Based Sliding Surface Reconstruction for Rapid Landslide Volume Estimation Q. Liu et al. https://doi.org/10.3390/geosciences16050205
48. A comparative geological, tectonic and geomorphological assessment of the Charlotte, Regent and Madina landslides, Western area, Sierra Leone Y. Lahai et al. https://doi.org/10.1186/s40677-021-00187-x
49. Dynamic landslide risk assessment (DLRA) using SBAS-InSAR method, integrated with geo-environmental data as temporal triggers in the Northwestern Himalayas, Pakistan A. Rahman et al. https://doi.org/10.1007/s10064-026-05205-6
50. Mechanism of subsidence-buckling and instability of slopes in thick-layered rigid rock under mining J. Deng et al. https://doi.org/10.1007/s11629-023-7952-6
51. Mapping precipitation-triggered landslide risks in global human settlements K. Wang et al. https://doi.org/10.1016/j.apgeog.2025.103886
52. Mapping of landslide potential in Pyeongchang-gun, South Korea, using machine learning meta-based optimization algorithms M. Fadhillah et al. https://doi.org/10.1016/j.ejrs.2022.03.008
53. Casual-Nuevo Alausí Landslide (Ecuador, March 2023): A Case Study on the Influence of the Anthropogenic Factors L. Pilatasig et al. https://doi.org/10.3390/geohazards6020028
54. Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review R. He et al. https://doi.org/10.1016/j.rockmb.2024.100144
55. Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers B. Pham et al. https://doi.org/10.1080/10106049.2020.1737972
56. Evaluation of the impact of climate change on rainfall for potential landslide triggering in Japan H. Tsunetaka et al. https://doi.org/10.1002/esp.6050
57. Leveraging local knowledge for landslide disaster risk reduction in an urban informal settlement in Manado, Indonesia E. MacAfee et al. https://doi.org/10.1016/j.ijdrr.2024.104710
58. 4D electrical resistivity to monitor unstable slopes in mountainous tropical regions: an example from Munnar, India A. Watlet et al. https://doi.org/10.1007/s10346-023-02029-3
59. Rapid Deformation Identification and Adaptive Filtering with GNSS TDCP Under Different Scenarios and Its Application in Landslide Monitoring M. Wu et al. https://doi.org/10.3390/rs17101751
60. The impact of the aggregation formula on indicator-based method for the assessment of building susceptibility to hydro-meteorological hazards R. Agliata et al. https://doi.org/10.1016/j.ijdrr.2022.102850
61. Differences in factors determining landslide hazards among three types of landslides in Japan Y. Shinohara & Y. Watanabe https://doi.org/10.1007/s11069-023-06075-x
62. Characteristics and causes of natural and human-induced landslides in a tropical mountainous region: the rift flank west of Lake Kivu (Democratic Republic of the Congo) J. Maki Mateso et al. https://doi.org/10.5194/nhess-23-643-2023
63. Preface: Landslide–transport network interactions F. Taylor et al. https://doi.org/10.5194/nhess-20-2585-2020
64. Exploring Various Deep Learning Models for High-Precision Landslide Tracing in Very-High Resolution Remote Sensing Imagery H. Nguyen et al. https://doi.org/10.1007/s12524-025-02267-z
65. Old but still active: > 18 ka history of rock slope failures affecting a flysch anticline M. Břežný et al. https://doi.org/10.1007/s10346-020-01483-7
66. Three-dimensional upper bound perturbation method for stability of slopes based on rigid translational-rotational coupled elements T. Tan et al. https://doi.org/10.1016/j.compgeo.2025.107720
67. Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, China L. Liu & J. Deng https://doi.org/10.1080/19475705.2024.2344804
68. Future changes in precipitation-caused landslide frequency in British Columbia S. Sobie https://doi.org/10.1007/s10584-020-02788-1
69. Imaging one of the largest Alpine slope instabilities with 3D seismic first-arrival traveltime tomography T. Kiers et al. https://doi.org/10.1016/j.jappgeo.2025.106081
70. When image correlation is needed: Unravelling the complex dynamics of a slow-moving landslide in the tropics with dense radar and optical time series A. Dille et al. https://doi.org/10.1016/j.rse.2021.112402
71. A revised landslide inventory of the Campania region (Italy) F. Fusco et al. https://doi.org/10.1038/s41597-023-02155-6
72. Precursors of catastrophic failure in intermittent creep landslides: a frictional evolution perspective J. Yang et al. https://doi.org/10.1680/jgeot.25.00636
73. Acid rain: an unsuspected factor predisposing Panzhihua airport landslide, China K. Li et al. https://doi.org/10.1007/s11356-021-13308-8
74. A modeling framework for assessing the future changes in the occurrence of extreme rain-induced landslides X. Zhang et al. https://doi.org/10.1016/j.gr.2025.03.009
75. A Regularization Method for Landslide Thickness Estimation L. Borgatti et al. https://doi.org/10.3390/jimaging10120314
76. 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
77. Mechanism and Predicting On the High and Steep Slope Landslides Formation: A Case Study of Bayan Obo Mine Y. Wei et al. https://doi.org/10.1007/s42461-026-01615-y
78. The research on landslide detection in remote sensing images based on improved DeepLabv3+ method Y. Li https://doi.org/10.1038/s41598-025-92822-y
79. Seismic, Field, and Remote Sensing Analysis of the 13 February 2024 Çöpler Gold Mine Landslide, Erzincan, Türkiye P. Büyükakpınar et al. https://doi.org/10.1785/0320250007
80. Landslides across the USA: occurrence, susceptibility, and data limitations B. Mirus et al. https://doi.org/10.1007/s10346-020-01424-4
81. Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature S. Wielandt et al. https://doi.org/10.3390/s22072814
82. Permafrost Degradation: Mechanisms, Effects, and (Im)Possible Remediation D. Baillarget & G. Scaringi https://doi.org/10.3390/land14101949
83. Time-Lapse Electrical Resistivity Tomography (TL-ERT) for Landslide Monitoring: Recent Advances and Future Directions V. Lapenna & A. Perrone https://doi.org/10.3390/app12031425
84. Bayesian inverse analysis with field observation for slope failure mechanism and reliability assessment under rainfall accounting for nonstationary characteristics of soil properties X. Liu et al. https://doi.org/10.1016/j.sandf.2025.101568
85. Optimization of Factors of Safety and Displacements of Unsaturated Soil Slopes by the Bioinspired Metaheuristic Method K. Waris & B. Basha https://doi.org/10.1061/IJGNAI.GMENG-11282
86. A national landslide inventory for Denmark G. Luetzenburg et al. https://doi.org/10.5194/essd-14-3157-2022
87. ERT-based experimental integrated approach for soil hydrological characterization in rainfall-induced shallow landslides prone areas V. Vivaldi et al. https://doi.org/10.1007/s10064-024-03627-8
88. Transfer learning-powered physics-informed neural network for forecasting and inversion analysis of rainfall-induced landslides S. Cao et al. https://doi.org/10.1016/j.cacaie.2026.100124
89. Timely prediction potential of landslide early warning systems with multispectral remote sensing: a conceptual approach tested in the Sattelkar, Austria D. Hermle et al. https://doi.org/10.5194/nhess-21-2753-2021
90. A multi-scale approach for deterministic analysis of landslide triggering and mass flow mechanism at Kaliasaur (Rudraprayag) A. Sajwan et al. https://doi.org/10.1007/s10346-023-02151-2
91. Synthetic libraries of urban landslide simulations to identify slope failure hotspots and drivers across spatial scales and landscapes E. Bozzolan et al. https://doi.org/10.1007/s10346-024-02327-4
92. Integrated Landslide Hazard Assessment Using Machine Learning, Rainfall Threshold, and RAMMS Based Rockfall Simulation in NH-05, Himachal Pradesh, India M. Singh et al. https://doi.org/10.1007/s12524-025-02173-4
93. Determination of the Probabilities of Landslide Events—A Case Study of Bhutan R. Sarkar & K. Dorji https://doi.org/10.3390/hydrology6020052
94. Landslide Hazard and Rainfall Threshold Assessment: Incorporating Shallow and Deep-Seated Failure Mechanisms with Physics-Based Models R. Marin et al. https://doi.org/10.3390/geosciences14100280
95. The Role of Soil Type in Triggering Shallow Landslides in the Alps (Lombardy, Northern Italy) F. Luino et al. https://doi.org/10.3390/land11081125
96. Landslide Hazard and Exposure Modelling in Data‐Poor Regions: The Example of the Rohingya Refugee Camps in Bangladesh R. Emberson et al. https://doi.org/10.1029/2020EF001666
97. Active landslide detection using integrated remote sensing technologies for a wide region and multiple stages: A case study in southwestern China H. Lu et al. https://doi.org/10.1016/j.scitotenv.2024.172709
98. Soil Moisture Retrieval in Slow-Moving Landslide Region Using SAOCOM $L$-Band: A Radiative Transfer Model Approach D. Rana et al. https://doi.org/10.1109/JSTARS.2025.3581934
99. Slope Stability assessment of a roadside landslide along Khodpe–Chainpur Road Section (NH-64), Sudurpashchim Province, Nepal R. KC et al. https://doi.org/10.1080/19475705.2026.2687940
100. A python system for regional landslide susceptibility assessment by integrating machine learning models and its application Z. Guo et al. https://doi.org/10.1016/j.heliyon.2023.e21542
101. Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review J. Song et al. https://doi.org/10.3390/app15031663
102. Comparative analysis of traditional and Gaussian Analytical Hierarchy Process (AHP) methods for landslide susceptibility assessment R. Marques-Carvalho et al. https://doi.org/10.1038/s41598-025-22136-6
103. Spatiotemporal assessment of multi hazard risk using graph based analysis for case studies in India H. Ekkirala & M. Ramesh https://doi.org/10.1038/s41598-026-35925-4
104. A framework for safer land use planning: Integrating RS-GIS and bivariate-MCDM for landslide susceptibility modeling in the Western Himalayan region of Himachal Pradesh, India S. Dhiman et al. https://doi.org/10.1016/j.kjs.2026.100592
105. Indicator-based approach for the assessment of intrinsic physical vulnerability of the built environment to hydro-meteorological hazards: Review of indicators and example of parameters selection for a sample area R. Agliata et al. https://doi.org/10.1016/j.ijdrr.2021.102199
106. Influence of buffer distance on environmental geological hazard susceptibility assessment Z. Wang et al. https://doi.org/10.1007/s11356-023-31739-3
107. Spatiotemporal prediction of landslide displacement using deep learning approaches based on monitored time-series displacement data: a case in the Huanglianshu landslide N. Xi et al. https://doi.org/10.1080/17499518.2023.2172186
108. Revealing the Effect of Typhoons on the Stability of Residual Soil Slope by Wind Tunnel Test Z. Guo et al. https://doi.org/10.3390/f15050791
109. Spatio-temporal cluster analysis of recent Italian landslides M. Tonini et al. https://doi.org/10.1080/17499518.2020.1861634
110. Laboratory Experiments of Surge Pressure Loads Acting on Downstream Dams and Reservoir Banks Caused by Landslides in Narrow, Elongated Reservoirs H. Wang et al. https://doi.org/10.3390/w17081133
111. Integrating multi-temporal LiDAR and AHP for landslide detection and susceptibility mapping in Prince George’s County, Maryland M. Okegbola et al. https://doi.org/10.1016/j.jnlssr.2026.100311
112. The Influence of Gravel Particle Diameter and Slope Angle on a Rockfall–Cushion Layer Collision S. Duan et al. https://doi.org/10.1007/s10706-022-02207-5
113. A framework to optimize a designed geotechnical system probabilistically using MLP-ANN and ELECTERE decision making – a nailed wall study P. Dastpak et al. https://doi.org/10.1016/j.geoai.2025.100014
114. Spatial understanding of historical and future landslide variation in Africa L. Nahayo et al. https://doi.org/10.1007/s11069-023-06126-3
115. Landslides after wildfire: initiation, magnitude, and mobility F. Rengers et al. https://doi.org/10.1007/s10346-020-01506-3
116. Overcoming the data limitations in landslide susceptibility modeling J. Woodard & B. Mirus https://doi.org/10.1126/sciadv.adt1541
117. Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling A. Felsberg et al. https://doi.org/10.5194/nhess-23-3805-2023
118. Study on characteristics and impact of Kalikhola landslide, Manipur, NE India, using UAV photogrammetry R. Pattanaik & Y. Singh https://doi.org/10.1007/s11069-024-06484-6
119. Combining interferometry and wave equation tomography for near surface characterization: 3-D imaging of the Harmalière alpine landslide G. Provenzano et al. https://doi.org/10.1093/gji/ggaf354
120. 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
121. Did they blow it? Time-lapse velocity variations during an open-pit mine slope failure using seismic noise interferometry T. De Wit & R. Snieder https://doi.org/10.26443/seismica.v5i1.1902
122. Causes and triggers of deep-seated hillslope instability in the tropics – Insights from a 60-year record of Ikoma landslide (DR Congo) A. Dille et al. https://doi.org/10.1016/j.geomorph.2019.106835
123. A hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines H. Wen et al. https://doi.org/10.1016/j.jenvman.2023.118177
124. Hazard assessment for regional typhoon-triggered landslides by using physically-based model – a case study from southeastern China Z. Guo et al. https://doi.org/10.1080/17499518.2023.2188465
125. Identification and Analysis of Landslides in the Ahai Reservoir Area of the Jinsha River Basin Using a Combination of DS-InSAR, Optical Images, and Field Surveys Y. Li et al. https://doi.org/10.3390/rs14246274
126. Performance evaluation for four GIS-based models purposed to predict and map landslide susceptibility: A case study at a World Heritage site in Southwest China Y. Jiao et al. https://doi.org/10.1016/j.catena.2019.104221
127. Shear surface undulations modulate clayey gouge strength and contribute to divergent landslide acceleration W. Schulz et al. https://doi.org/10.1016/j.enggeo.2025.108353
128. Relationship between continuous or discontinuous of controlling factors and landslide susceptibility in the high-cold mountainous areas, China H. Shu et al. https://doi.org/10.1016/j.ecolind.2025.113313
129. Topographic Analysis of Landslide Distribution Using AW3D30 Data A. Nonomura et al. https://doi.org/10.3390/geosciences10040115
130. 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
131. A Spatial Landslide Risk Assessment Based on Hazard, Vulnerability, Exposure, and Adaptive Capacity T. Tran et al. https://doi.org/10.3390/su16219574
132. Multi-coal seam mining-induced collapse and deformation evolution of high steep slopes of the Shaomi collapse, Guizhou, China B. Shen et al. https://doi.org/10.1007/s10346-024-02381-y
133. Granular flow simulation in a centrifugal acceleration field M. Cabrera et al. https://doi.org/10.1680/jgeot.18.P.260
134. Experimental study on the stability of vegetated earthen slopes under intense rainfall X. Song & Y. Tan https://doi.org/10.1016/j.still.2024.106028
135. Monitoring strain evolution in water-sand systems using distributed acoustic sensing for geohazard early warning Y. Li et al. https://doi.org/10.1038/s41598-025-34069-1
136. SLOPE STABILITY ANALYSIS USING LIMIT EQUILIBRIUM AND FINITE ELEMENT METHODS: A CASE STUDY OF THE KHERRATA LANDSLIDE, ALGERIA M. Djabri et al. https://doi.org/10.30572/2018/KJE/170307
137. Calibration of the microparameters of the discrete element method using a relevance vector machine and its application to rockfill materials C. Ma et al. https://doi.org/10.1016/j.advengsoft.2020.102833
138. Rainfall Infiltration through Stratified Colluvial Deposits: Analytical Approach vs. Numerical Modelling P. Paronuzzi et al. https://doi.org/10.3390/geosciences14020053
139. Landslide timing and rainfall regimes in a rapidly urbanizing tropical mountain valley in Colombia E. Aristizábal et al. https://doi.org/10.1007/s11069-026-08028-6
140. An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India S. Ali et al. https://doi.org/10.1007/s11069-022-05360-5
141. Rapid Detection of Landslides for the Timely Response of Disaster Mitigation and Relief D. Liu & G. Liu https://doi.org/10.1061/NHREFO.NHENG-2263
142. Analysis of Changes in Landslide Susceptibility according to Land Use over 38 Years in Lixian County, China J. Liu et al. https://doi.org/10.3390/su131910858
143. The 2020 Hpakant Jade Mine Disaster, Myanmar: A multi-sensor investigation for slope failure Y. Lin et al. https://doi.org/10.1016/j.isprsjprs.2021.05.015
144. Impact of topography on in situ soil wetness measurements for regional landslide early warning – a case study from the Swiss Alpine Foreland A. Wicki et al. https://doi.org/10.5194/nhess-23-1059-2023
145. Utility of a swath laser rangefinder for characterizing mass movement flow depth and landslide initiation M. Obryk et al. https://doi.org/10.1007/s10346-025-02533-8
146. Slope-specific rainfall thresholds for regional landslide early warning systems X. Xie et al. https://doi.org/10.1016/j.enggeo.2025.108249
147. A Physics-Informed Online Learning Framework for Landslide Displacement Prediction J. Zhou et al. https://doi.org/10.3390/app16126003
148. Atmospheric rivers catalyze snowmelt and contribute to chains of landslides H. Aslan et al. https://doi.org/10.1016/j.catena.2025.109503
149. Synergistic effect of rainfall and pipe leakage erosion on the formation and development of loess caves in Weinan city, Loess Plateau, Northwest China G. Zhou et al. https://doi.org/10.1007/s12665-023-11130-8
150. Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models F. Huang et al. https://doi.org/10.3390/ijgi9060377
151. A Conditional Probability-Based Model for Mountainous Geological Hazard Susceptibility Assessment Y. Wang et al. https://doi.org/10.3390/app152312653
152. 30-year record of Himalaya mass-wasting reveals landscape perturbations by extreme events J. Jones et al. https://doi.org/10.1038/s41467-021-26964-8
153. Stability analysis of complex terrain slope based on multi-source point cloud fusion W. Zan et al. https://doi.org/10.1007/s11629-022-7307-8
154. “Understanding livelihood insecurity due to landslides in the mid-hill of Nepal: A case study of Bahrabise Municipality” M. Shrestha et al. https://doi.org/10.1016/j.ijdrr.2024.104399
155. Deformation and Failure Mechanism of a Massive Ancient Anti-Dip River-Damming Landslide in the Upper Jinsha River Y. Li et al. https://doi.org/10.3390/su142013048
156. Precipitation Characteristics and Future Changes Over the Southern Slope of Tibetan Plateau Simulated by a High‐Resolution Global Nonhydrostatic Model Y. Na et al. https://doi.org/10.1029/2020JD033630
157. Accelerating high-fidelity debris-flow modeling with a super-resolution deep-learning framework Y. Lv et al. https://doi.org/10.1016/j.enggeo.2026.108851
158. Development and Application of an Automated Check-Dam Scenario Analysis Program for Debris-Flow Hazards S. Lee et al. https://doi.org/10.9798/KOSHAM.2026.26.3.33
159. Resilience assessment of reinforced concrete buildings in the Indian Himalayan region under flow-type landslides R. Kumar et al. https://doi.org/10.1177/13694332251375217
160. Bias-reduced landslide susceptibility mapping in data-scarce regions via multi-source inventory integration Y. Xiong et al. https://doi.org/10.1080/19475705.2026.2717802
161. Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region S. Saha et al. https://doi.org/10.1007/s00477-022-02212-3
162. Landslide susceptibility and mobilization rates in the Mount Elgon region, Uganda J. Broeckx et al. https://doi.org/10.1007/s10346-018-1085-y
163. Landslide Susceptibility Assessment of a Part of the Western Ghats (India) Employing the AHP and F-AHP Models and Comparison with Existing Susceptibility Maps S. Bhagya et al. https://doi.org/10.3390/land12020468
164. Increasing landslide deformation and activity in a changing local environment: a case study of Zhouqu County in the Bailong River Basin Z. Liu et al. https://doi.org/10.1007/s10064-025-04106-4
165. Geospatial distribution of landslide susceptibility modeling based on Remote Sensing and Geographic Information System K. Shitu & A. Tariq https://doi.org/10.1016/j.jafrearsci.2025.105732
166. An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest X. Xiao et al. https://doi.org/10.1080/19475705.2024.2347421
167. Mobilization rates of landslides in a changing tropical environment: 60-year record over a large region of the East African Rift A. Depicker et al. https://doi.org/10.1016/j.geomorph.2024.109156
168. Factors controlling the formation and movement of clustered shallow landslides triggered by the extreme rainstorm in July 2023 in Beijing, China H. Ma & F. Wang https://doi.org/10.1016/j.geomorph.2025.109728
169. Introduce a framework for landslide risk assessment using geospatial analysis: a case study from Kegalle District, Sri Lanka E. Perera et al. https://doi.org/10.1007/s40808-020-00811-z
170. Analyzing the effects of eco-DRR practices on slope stability in a landslide-prone transport corridor in the Western Ghats, India A. Krishnan et al. https://doi.org/10.64866/j.ijdscr.2025.10004
171. A Regional-Scale Landslide Early Warning System Based on the Sequential Evaluation Method: Development and Performance Analysis J. Park et al. https://doi.org/10.3390/app10175788
172. A geo-domain constrained similarity framework for sample augmentation in landslide susceptibility mapping Y. Yang et al. https://doi.org/10.1080/19475705.2026.2721824
173. An extended kinematic inventory, magnitude-frequency curves, and damage assessment of rock slope failures in the central Spanish Pyrenees J. Guerrero et al. https://doi.org/10.1016/j.geomorph.2026.110172
174. Estimating landslide sliding distance based on an improved Heim sled model Z. Zhang et al. https://doi.org/10.1016/j.catena.2021.105401
175. Spatiotemporal distribution characteristics, causes, and prevention advice of fatal geohazards in Jiangxi Province, China C. Shao et al. https://doi.org/10.1016/j.scitotenv.2022.155337
176. A Spike-Inspired Adaptive Spatial Suppression Framework for Large-Scale Landslide Extraction M. Gao et al. https://doi.org/10.3390/rs18010129
177. Road network resilience assessment based on full-scenario disaster simulation: landslide movement, emergency, and stabilization X. Chen et al. https://doi.org/10.1016/j.ress.2026.112572
178. A data-driven evaluation of post-fire landslide susceptibility E. Culler et al. https://doi.org/10.5194/nhess-23-1631-2023
179. A frictional weakening law of breakable granular flow influenced by shear velocity and normal stress Z. Liu et al. https://doi.org/10.1016/j.jrmge.2024.12.008
180. Türkiye Cumhuriyeti’nin 100. Yılında Türkiye Adresli Yayınların Dünya Heyelan Literatürüne Katkısı C. Gökçeoğlu https://doi.org/10.24232/jmd.1443848
181. Two Nitsche-based mixed finite element discretizations for the seepage problem in Richards’ equation F. Gatti et al. https://doi.org/10.1016/j.cma.2024.117368
182. Detecting the Vertical Structure of Extreme Precipitation in the Headwater Area of Yellow River Using the Dual‐Frequency Precipitation Radar Onboard the Global Precipitation Measurement Mission J. Song et al. https://doi.org/10.1002/joc.8675
183. Shear strain prediction of reservoir landslide based on FBG monitoring and bagging-MLP algorithm J. Wang et al. https://doi.org/10.1007/s10064-024-04076-z
184. Refined Zoning of Landslide Susceptibility: A Case Study in Enshi County, Hubei, China Z. Wang et al. https://doi.org/10.3390/ijerph19159412
185. Correlation between Acoustic Emission Behaviour and Dynamics Model during Three-Stage Deformation Process of Soil Landslide L. Deng et al. https://doi.org/10.3390/s21072373
186. 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
187. Assessing the impact of precipitation variability on landslide hazards in urbanized regions B. Du et al. https://doi.org/10.1016/j.jag.2025.104360
188. Regional heterogeneity-aware domain adversarial training for trans-regional landslide susceptibility assessment J. Tu et al. https://doi.org/10.1080/19475705.2026.2717922
189. The human cost of global warming: Deadly landslides and their triggers (1995–2014) U. Haque et al. https://doi.org/10.1016/j.scitotenv.2019.03.415
190. Landslide detection based on pixel-level contrastive learning for semi-supervised semantic segmentation in wide areas J. Lv et al. https://doi.org/10.1007/s10346-024-02425-3
191. Effect of Uncertainty in Geotechnical and Flow Parameters on Landslide-induced Force for Building Response Analysis Under Flow-type Landslides Using Uncoupled Approach M. Kulariya et al. https://doi.org/10.1007/s40098-024-01074-6
192. A Physical and Numerical Investigation of Flow–Barrier Interaction for the Design of a Multiple-Barrier System C. Ng et al. https://doi.org/10.1061/(ASCE)GT.1943-5606.0002932
193. Flash flood and landslide susceptibility analysis for a mountainous roadway in Vietnam using spatial modeling C. Luu et al. https://doi.org/10.1016/j.qsa.2023.100083
194. Integrating climate change, land-use dynamics, and explainable AI for future landslide susceptibility assessment in the Western Ghats S. Kushwaha et al. https://doi.org/10.1016/j.pce.2026.104764
195. Four-Year Monitoring Study of Shallow Landslide Hazards Based on Hydrological Measurements in a Weathered Granite Soil Slope in South Korea K. Kim et al. https://doi.org/10.3390/w13172330
196. Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression C. Chalkias et al. https://doi.org/10.1007/s10064-020-01733-x
197. 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
198. Conventional and advanced geospatial techniques for landslide detection and modeling: a comprehensive overview M. Shafapourtehrany et al. https://doi.org/10.1186/s40677-025-00347-3
199. Improving Landslide Susceptibility Assessment Through Non-Landslide Sampling Strategies L. Tu et al. https://doi.org/10.3390/land14102059
200. Research trends and knowledge gaps in earthquake-induced landslides in Nepal: a bibliometric review B. Bohara et al. https://doi.org/10.1007/s41062-026-02866-w
201. Temporal patterns of nonseismically triggered landslides in Shaanxi Province, China H. Qiu et al. https://doi.org/10.1016/j.catena.2019.104356
202. Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy M. Moreno et al. https://doi.org/10.1016/j.scitotenv.2023.169166
203. Landslide mapping and analysis along the China-Pakistan Karakoram Highway based on SBAS-InSAR detection in 2017 X. Su et al. https://doi.org/10.1007/s11629-021-6686-6
204. Geological Disaster Risk Assessment Under Extreme Precipitation Conditions in the Ili River Basin X. Li et al. https://doi.org/10.3390/ijgi14090346
205. Bayes Olasılık Modeli ve Frekans Oranı (FO) Yöntemi ile Esmahanım Deresi Havzası’nın (Düzce) Heyelan Duyarlık Analizi V. Avci https://doi.org/10.48123/rsgis.1708584
206. A participatory approach to determine the use of road cut slope design guidelines in Nepal to lessen landslides E. Robson et al. https://doi.org/10.5194/nhess-25-949-2025
207. Landslide susceptibility mapping using physics-guided machine learning: a case study of a debris flow event in Colorado Front Range T. Pei & T. Qiu https://doi.org/10.1007/s11440-024-02384-y
208. Research on the stability of loess landslides under seismic action based on simplified bishop method G. Hu et al. https://doi.org/10.3389/feart.2024.1490558
209. Integration of effective antecedent rainfall to improve the performance of rainfall thresholds for landslide early warning in Wanzhou District, China X. Liang et al. https://doi.org/10.1016/j.ijdrr.2025.105317
210. Utilizing deep learning for intelligent monitoring and early warning of slope disasters in public space design W. Ting & Y. Wang https://doi.org/10.3389/fenvs.2025.1536481
211. Systematic Quantification and Assessment of Digital Image Correlation Performance for Landslide Monitoring D. Hermle et al. https://doi.org/10.3390/geosciences13120371
212. InSAR phase gradient reveals fault‐zone controls on the spatial distribution of slow‐moving landslides in the active orogenic region of Hazara‐Kashmir, Pakistan S. Ahmad et al. https://doi.org/10.1002/esp.6015
213. Site scale landslide deformation and strain analysis using MT-InSAR and GNSS approach – A case study V. Kumar Maurya et al. https://doi.org/10.1016/j.asr.2022.05.028
214. Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China W. Xie et al. https://doi.org/10.1007/s11069-021-04862-y
215. Rainfall-induced landslide susceptibility mapping using machine learning algorithms and comparison of their performance in Hilly area of Fujian Province, China P. Ye et al. https://doi.org/10.1007/s11069-022-05332-9
216. Mechanics of coseismic and postseismic acceleration of active landslides M. Kohler & A. Puzrin https://doi.org/10.1038/s43247-023-00797-3
217. Preliminary inferences on the August 5, 2025, catastrophic debris flow in Dharali, India: insights from the satellite imageries and geomorphological analysis of the Kheer Gad watershed M. Devi et al. https://doi.org/10.1007/s10346-026-02723-y
218. Mitigating GNSS multipath in landslide areas: A novel approach considering mutation points at different stages X. Liu et al. https://doi.org/10.1007/s10346-023-02117-4
219. Lethality level casualty assessment method for earthquake landslide hazards based on the expansion effect of the mortality rate X. Chaoxu et al. https://doi.org/10.1038/s41598-025-08209-6
220. Surface wave monitoring using ambient noise for detecting temporal variations in underground structures in landslide area C. Feng et al. https://doi.org/10.1016/j.enggeo.2024.107706
221. Assessing landslide susceptibility based on hybrid multilayer perceptron with ensemble learning H. Hong https://doi.org/10.1007/s10064-023-03409-8
222. Landslide topology uncovers failure movements K. Bhuyan et al. https://doi.org/10.1038/s41467-024-46741-7
223. Prototype of an IoT-Based Low-Cost Sensor Network for the Hydrological Monitoring of Landslide-Prone Areas P. Marino et al. https://doi.org/10.3390/s23042299
224. Assessing Historical Landslide Risk Management Based on Trigger Magnitude and Consequences: A Case Study from the Rokko Mountains, Kobe, Japan T. Sato & Y. Shuin https://doi.org/10.1007/s10706-024-02932-z
225. Exploring the dynamic impact of urbanization on landslide susceptibility in Sichuan Province using an explainable XGBoost model Y. He et al. https://doi.org/10.1016/j.enggeo.2025.108372
226. Empowerment of Farmer Institutions in Improving Farmer’s Bargaining Position M. Sasmi et al. https://doi.org/10.1088/1755-1315/1177/1/012001
227. Deciphering seasonal effects of triggering and preparatory precipitation for improved shallow landslide prediction using generalized additive mixed models S. Steger et al. https://doi.org/10.5194/nhess-23-1483-2023
228. Geographically Weighted Random Forest Based on Spatial Factor Optimization for the Assessment of Landslide Susceptibility F. Lu et al. https://doi.org/10.3390/rs17091608
229. 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
230. Landslide mapping from multi-sensor data through improved change detection-based Markov random field P. Lu et al. https://doi.org/10.1016/j.rse.2019.111235
231. Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms X. Zhao et al. https://doi.org/10.1080/10106049.2025.2507919
232. Predicting the thickness of shallow landslides in Switzerland using machine learning C. Schaller et al. https://doi.org/10.5194/nhess-25-467-2025
233. Landslide Susceptibility Mapping in Xinjiang: Identifying Critical Thresholds and Interaction Effects Among Disaster-Causing Factors X. Feng et al. https://doi.org/10.3390/land14030555
234. LADI: Landslide displacement interpolation through a spatial-temporal Kalman filter A. Senogles et al. https://doi.org/10.1016/j.cageo.2023.105451
235. Effect of groundwater table fluctuation on slope instability: a comprehensive 3D simulation approach for Kotropi landslide, India S. Panda et al. https://doi.org/10.1007/s10346-022-01993-6
236. Forecasting landslide deformation by integrating domain knowledge into interpretable deep learning considering spatiotemporal correlations Z. Ma & G. Mei https://doi.org/10.1016/j.jrmge.2024.02.034
237. Identification of Potential Landslides in the Gaizi Valley Section of the Karakorum Highway Coupled with TS-InSAR and Landslide Susceptibility Analysis K. Lin et al. https://doi.org/10.3390/rs16193653