132 citations as recorded by crossref.

1. Physically-based landslide susceptibility analysis using Monte Carlo simulation in a tropical mountain basin R. Marin & Á. Mattos https://doi.org/10.1080/17499518.2019.1633582
2. Machine learning-based assessment of three-dimensional slope stability using geometric features under heavy rainfall X. Li et al. https://doi.org/10.1016/j.jrmge.2025.12.022
3. Machine learning-based rainfall-induced landslide susceptibility model and short-term early warning assessment in South Korea S. Lee et al. https://doi.org/10.1007/s10346-025-02513-y
4. Visualization analysis of rainfall-induced landslides hazards based on remote sensing and geographic information system-an overview Z. Yang et al. https://doi.org/10.1080/17538947.2023.2229797
5. Rainfall-induced landslides by deficit field matric suction in unsaturated soil slopes J. Kim et al. https://doi.org/10.1007/s12665-017-7127-2
6. Improved Method of Defining Rainfall Intensity and Duration Thresholds for Shallow Landslides Based on TRIGRS S. Zhang et al. https://doi.org/10.3390/w14040524
7. A novel DEM enhancement methodology to improve physics-based susceptibility modeling of rainfall-induced landslide along anthropogenically modified slopes A. Ajith et al. https://doi.org/10.1139/cgj-2025-0008
8. A combined triggering-propagation modeling approach for the assessment of rainfall induced debris flow susceptibility L. Stancanelli et al. https://doi.org/10.1016/j.jhydrol.2017.04.038
9. Estimation of Rainfall-Induced Landslides Using the TRIGRS Model A. Dikshit et al. https://doi.org/10.1007/s41748-019-00125-w
10. Current water uses, related risks, and management options for Seoul megacity, Korea J. Lee et al. https://doi.org/10.1007/s12665-017-7192-6
11. 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
12. A Non-Linear, Time-Variant Approach to Simulate the Rainfall-Induced Slope Failure of an Unsaturated Soil Slope: A Case Study in Sapa, Vietnam T. Tran et al. https://doi.org/10.20965/jdr.2021.p0512
13. Determination of average growth rate based on statistical relationships using geomorphological and geotechnical variables in predictive debris flow simulations J. Park et al. https://doi.org/10.1016/j.geomorph.2023.108955
14. 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
15. Three-dimensional, time-dependent modeling of rainfall-induced landslides over a digital landscape: a case study T. Tran et al. https://doi.org/10.1007/s10346-017-0931-7
16. A probabilistic multidimensional approach to quantify large wood recruitment from hillslopes in mountainous-forested catchments A. Cislaghi et al. https://doi.org/10.1016/j.geomorph.2018.01.009
17. 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
18. Coupled modeling framework for proactive design of debris-flow barrier placements D. Lee et al. https://doi.org/10.1038/s41598-025-15290-4
19. Influence of hydraulic properties on physically modelling slope stability and the definition of rainfall thresholds for shallow landslides R. Marin & M. Velásquez https://doi.org/10.1016/j.geomorph.2019.106976
20. 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
21. 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
22. Understanding the sensitivity to the soil properties and rainfall conditions of two physically-based slope stability models R. Marin et al. https://doi.org/10.18273/revbol.v44n1-2022004
23. Exploring Influence of Sampling Strategies on Event-Based Landslide Susceptibility Modeling J. Lai et al. https://doi.org/10.3390/ijgi8090397
24. Integrating geoinformatics and numerical modelling for landslide back-analysis and forecasting: a proactive mitigation study of the Shiv Bawri landslide A. Sajwan & G. Ramana https://doi.org/10.1007/s10346-024-02321-w
25. GIS-based landslide susceptibility model considering effective contributing area for drainage time A. Pradhan & Y. Kim https://doi.org/10.1080/10106049.2017.1303089
26. Shallow Landslide Susceptibility Models Based on Artificial Neural Networks Considering the Factor Selection Method and Various Non-Linear Activation Functions D. Lee et al. https://doi.org/10.3390/rs12071194
27. Mobility characteristics of rainfall-triggered shallow landslides in a forest area in Mengdong, China H. Bingli et al. https://doi.org/10.1007/s10346-024-02267-z
28. Landslide Distribution and Susceptibility Assessment in NW Pakistan: Insights from Field Observations and Factor Analysis M. Ahmad et al. https://doi.org/10.15446/esrj.v29n1.117884
29. Effect of Digital Elevation Model Resolution on Shallow Landslide Modeling Using TRIGRS T. Viet et al. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000233
30. Dynamic hazard assessment of group-occurring debris flows based on a coupled model Y. Nie et al. https://doi.org/10.1007/s11069-021-04558-3
31. Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility J. Sun et al. https://doi.org/10.1007/s11069-020-04498-4
32. Rainfall thresholds for shallow landslides considering rainfall temporal patterns B. Zhao et al. https://doi.org/10.1007/s10064-025-04144-y
33. Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models C. Polykretis & C. Chalkias https://doi.org/10.1007/s11069-018-3299-7
34. Site-specific to local-scale shallow landslides triggering zones assessment using TRIGRS M. Bordoni et al. https://doi.org/10.5194/nhess-15-1025-2015
35. Influence of subsurface flow by Lidar DEMs and physical soil strength considering a simple hydrologic concept for shallow landslide instability mapping S. Kim et al. https://doi.org/10.1016/j.catena.2019.104137
36. GIS based landslide susceptibility mapping and assessment using weighted overlay method in Wayanad: A part of Western Ghats, Kerala T. Arumugam et al. https://doi.org/10.1016/j.uclim.2023.101508
37. Regional-scale back-analysis using TRIGRS: an approach to advance landslide hazard modeling and prediction in sparse data regions L. Weidner et al. https://doi.org/10.1007/s10346-018-1044-7
38. Numerical Analysis of Jetty Platform Countermeasure Effort at Muaro Kualo Area in Bengkulu City, Indonesia L. Mase & V. Febriyanto https://doi.org/10.1007/s40515-022-00258-y
39. Spatiotemporal decoupling of rainfall patterns and shallow landslide stability: Lessons learned from calibrated ensemble physically-based models M. Wu et al. https://doi.org/10.1016/j.jrmge.2025.08.011
40. Spatial and Temporal Analysis Prediction of Landslide Susceptibility Using Rainfall Infiltration and Grid-based Slope Stability Methods in West Bandung area of West Java-Indonesia K. Hermawan et al. https://doi.org/10.1088/1755-1315/1173/1/012031
41. High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea M. Park et al. https://doi.org/10.5194/amt-10-1575-2017
42. Numerical Simulation of the Topographical Change in Korea Mountain Area by Intense Rainfall and Consequential Debris Flow B. Jun https://doi.org/10.1155/2016/9363675
43. Multi-horizon prediction of rainfall-induced slope responses using physical state-space modeling Y. Cao et al. https://doi.org/10.1016/j.cacaie.2026.100143
44. Assessing two methods of defining rainfall intensity and duration thresholds for shallow landslides in data-scarce catchments of the Colombian Andean Mountains R. Marin et al. https://doi.org/10.1016/j.catena.2021.105563
45. Assessing global parameters of slope stability model using Earth data observations for forecasting rainfall – induced shallow landslides J. Thomas et al. https://doi.org/10.1016/j.jappgeo.2023.104994
46. Coupled model for simulation of landslides and debris flows at local scale D. Park et al. https://doi.org/10.1007/s11069-016-2150-2
47. GIS based assessment of rainfall-induced landslide susceptibility of sensitive marine clays: a case study M. Al-Umar et al. https://doi.org/10.1080/17486025.2021.1955152
48. Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis M. Conforti & F. Ietto https://doi.org/10.3390/geosciences11080333
49. Comparing the performance of TRIGRS and TiVaSS in spatial and temporal prediction of rainfall-induced shallow landslides T. Tran et al. https://doi.org/10.1007/s12665-017-6635-4
50. Liquid and plastic limits of mountain soils as a function of the soil and horizon type S. Stanchi et al. https://doi.org/10.1016/j.catena.2015.07.021
51. Identifying human activities and rainfall impacts in landslide: a case study from southwestern China X. Wang et al. https://doi.org/10.1007/s11069-025-07728-9
52. A Physico-Data TRIGRS model for county-scale shallow landslide dynamic hazard assessment F. Huang et al. https://doi.org/10.1080/17499518.2026.2674872
53. Applicability and performance of deterministic and probabilistic physically based landslide modeling in a data-scarce environment of the Colombian Andes R. Marin et al. https://doi.org/10.1016/j.jsames.2021.103175
54. Regional Analyses of Rainfall-Induced Landslide Initiation in Upper Gudbrandsdalen (South-Eastern Norway) Using TRIGRS Model L. Schilirò et al. https://doi.org/10.3390/geosciences11010035
55. Engineering geological mapping procedures in data-scarce hillsides for shallow landslide assessments: Applications in southeastern Brazil A. da Silva et al. https://doi.org/10.1016/j.jsames.2021.103489
56. iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction G. Chen et al. https://doi.org/10.5194/gmd-16-2915-2023
57. Analogy between grid-based modeling of landslide and avalanche using GIS with surface flow analysis S. Kim & H. Park https://doi.org/10.1007/s10064-017-1144-y
58. Prediction of rainfall‐induced shallow landslides in the Loess Plateau, Yan'an, China, using the TRIGRS model J. Zhuang et al. https://doi.org/10.1002/esp.4050
59. Accelerating Effect of Vegetation on the Instability of Rainfall-Induced Shallow Landslides J. Zhang et al. https://doi.org/10.3390/rs14225743
60. Evaluation of spatiotemporal effects of soil depth on shallow landslides and debris flows via coupled numerical analysis S. Lee et al. https://doi.org/10.1016/j.pdisas.2025.100470
61. Hazard prediction modeling for typhoon-triggered shallow landslides by integrating physically-based model and extreme rainfall analysis T. Zeng et al. https://doi.org/10.1007/s10064-025-04371-3
62. Three-dimensional slope stability analysis incorporating unsaturated soil properties in Singapore S. Ip et al. https://doi.org/10.1080/17499518.2020.1737880
63. Integrating TRIGRS and RAMMS for the spatiotemporal prediction of rainfall induced landslides and landslide trajectory: a case study A. Musaib et al. https://doi.org/10.1007/s11069-025-07779-y
64. GIS-based assessment of debris flow hazards in Kulekhani Watershed, Nepal B. Paudel et al. https://doi.org/10.1007/s11069-020-03867-3
65. Integrating FR, MFR and IV Models for Landslide Susceptibility Zonation Mapping in Joshimath Watershed, Uttarakhand, India I. Khan et al. https://doi.org/10.1007/s40996-024-01708-y
66. Enhancing Landslide Hazard Assessment Using Monte Carlo Simulations and Improved Soil Thickness Mapping in Uttarakhand, India K. Gupta & N. Satyam https://doi.org/10.1061/NHREFO.NHENG-2449
67. Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan S. Pasang & P. Kubíček https://doi.org/10.3390/geosciences10110430
68. A new slope unit extraction method for regional landslide analysis based on morphological image analysis K. Wang et al. https://doi.org/10.1007/s10064-018-1389-0
69. Time-resolved triggering and runout analysis of rainfall-induced shallow landslides G. La Porta et al. https://doi.org/10.1007/s11440-023-01996-0
70. The effects of debris flow in the Republic of Korea and some issues for successful risk reduction S. Lee & M. Winter https://doi.org/10.1016/j.enggeo.2019.01.003
71. Combining rainfall-induced shallow landslides and subsequent debris flows for hazard chain prediction W. Zhou et al. https://doi.org/10.1016/j.catena.2022.106199
72. A Case Study of Slope Stability Improvement in Central Bengkulu Landslide in Indonesia L. Mase et al. https://doi.org/10.1007/s40515-021-00186-3
73. A method to develop the input parameter database for site-specific debris flow hazard prediction under extreme rainfall N. Vasu et al. https://doi.org/10.1007/s10346-018-0971-7
74. An open-source and QGIS-integrated physically based model for Spatial Prediction of Rainfall-Induced Shallow Landslides (SPRIn-SL) L. Raimondi et al. https://doi.org/10.1016/j.envsoft.2022.105587
75. Does parameterization influence the performance of slope stability model results? A case study in Rio de Janeiro, Brazil C. de Lima Neves Seefelder et al. https://doi.org/10.1007/s10346-016-0783-6
76. Effect of basin morphometric parameters on physically-based rainfall thresholds for shallow landslides R. Marin et al. https://doi.org/10.1016/j.enggeo.2020.105855
77. Sensitivity analysis of regional rainfall-induced landslide based on UAV photogrammetry and LSTM neural network L. Zhao et al. https://doi.org/10.1007/s11629-023-7991-z
78. Microstructural Reconstruction and Interfacial Regulation in a CaCl2–Sodium Polyacrylate Organic–Inorganic Composite System for High-Liquid-Limit Clay L. Zhang et al. https://doi.org/10.3390/jcs10050248
79. Residential community-based quantitative risk assessment of rainfall-induced loess landslides for the suburban residential areas of Chengguan district in Lanzhou city with an innovative framework J. He et al. https://doi.org/10.1016/j.enggeo.2026.108785
80. Study of Earthquake Landslide Hazard by Defining Potential Landslide Thickness Using Excess Topography: A Case Study of the 2014 Ludian Earthquake Area, China P. Zhang et al. https://doi.org/10.3390/rs16162951
81. Triggering of Rain-Induced Landslides, with Applications in Southern Italy A. D’Ippolito et al. https://doi.org/10.3390/w15020277
82. Double-index rainfall warning and probabilistic physically based model for fast-moving landslide hazard analysis in subtropical-typhoon area T. Zeng et al. https://doi.org/10.1007/s10346-023-02187-4
83. Climate Change Adaptation to Extreme Rainfall Events on a Local Scale in Namyangju, South Korea T. Lee et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001906
84. A Simple Deposition Model for Debris Flow Simulation Considering the Erosion–Entrainment–Deposition Process S. Lee et al. https://doi.org/10.3390/rs14081904
85. TRIGRS Application for landslide susceptibility mapping K. Sugiarti & S. Sukristiyanti https://doi.org/10.1088/1755-1315/118/1/012040
86. Influence of DEM resolution on landslide simulation performance based on the Scoops3D model H. Qiu et al. https://doi.org/10.1080/19475705.2022.2097451
87. Prediction of spatiotemporal stability and rainfall threshold of shallow landslides using the TRIGRS and Scoops3D models J. He et al. https://doi.org/10.1016/j.catena.2020.104999
88. Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources K. An et al. https://doi.org/10.3390/su10020293
89. Assessment of a Dynamic Physically Based Slope Stability Model to Evaluate Timing and Distribution of Rainfall-Induced Shallow Landslides J. Thomas et al. https://doi.org/10.3390/ijgi12030105
90. Spatial model integration for shallow landslide susceptibility and its runout using a GIS-based approach in Yongin, Korea A. Pradhan et al. https://doi.org/10.1080/10106049.2016.1155658
91. Landslide Vulnerability Mapping for Landslide Victim Relocation R. Hidayat https://doi.org/10.1088/1757-899X/771/1/012054
92. A GIS-Based Framework for Real-Time Debris-Flow Hazard Assessment for Expressways in Korea H. Kim et al. https://doi.org/10.1007/s13753-016-0096-3
93. Bridging data gaps in landslide early warning: Physics-derived rainfall thresholds under rainfall uncertainty H. Zou et al. https://doi.org/10.1016/j.enggeo.2026.108608
94. GIS-based modeling of snowmelt-induced landslide susceptibility of sensitive marine clays M. Al-Umar et al. https://doi.org/10.1186/s40677-020-0142-8
95. Prediction of Shallow Rainfall-Induced Landslides Using Shear Strength of Unsaturated Soil S. Ravindran & I. Gratchev https://doi.org/10.1007/s40098-020-00468-6
96. Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling S. Park et al. https://doi.org/10.3390/su11205659
97. Different Approaches to Use Morphometric Attributes in Landslide Susceptibility Mapping Based on Meso-Scale Spatial Units: A Case Study in Rio de Janeiro (Brazil) V. Canavesi et al. https://doi.org/10.3390/rs12111826
98. Reliability and Sensitivity Analysis of Rainfall-Induced Shallow Landslides Using Trigrs Model. Case Study: San Antonio De Prado L. Osorio-Ríos et al. https://doi.org/10.2139/ssrn.4076168
99. Volume Estimation of Landslide Affected Soil Moisture Using TRIGRS: A Case Study of Longxi River Small Watershed in Wenchuan Earthquake Zone, China T. Sun et al. https://doi.org/10.3390/w13010071
100. A hybrid feature selection algorithm integrating an extreme learning machine for landslide susceptibility modeling of Mt. Woomyeon, South Korea N. Vasu & S. Lee https://doi.org/10.1016/j.geomorph.2016.03.023
101. Probabilistic and physically-based modelling of rainfall-induced landslide susceptibility using integrated GIS-FORM algorithm H. Cui et al. https://doi.org/10.1007/s10346-024-02226-8
102. Physically based and distributed rainfall intensity and duration thresholds for shallow landslides R. Marin https://doi.org/10.1007/s10346-020-01481-9
103. Використання геоінформаційних технологій для дослідження зсувних процесів О. Кравець https://doi.org/10.36930/40300220
104. Probabilistic model calibration of spatial variability for a physically-based landslide susceptibility model J. Luo et al. https://doi.org/10.1080/17499518.2021.1988986
105. Landslide susceptibility modeling in a complex mountainous region of Sikkim Himalaya using new hybrid data mining approach A. Islam et al. https://doi.org/10.1080/10106049.2021.2009920
106. A comparative study on the integrative ability of the analytical hierarchy process, weights of evidence and logistic regression methods with the Flow-R model for landslide susceptibility assessment H. Do et al. https://doi.org/10.1080/19475705.2020.1846086
107. Improving Spatial Landslide Prediction with 3D Slope Stability Analysis and Genetic Algorithm Optimization: Application to the Oltrepò Pavese N. Palazzolo et al. https://doi.org/10.3390/w13060801
108. Influence of digital elevation models on the simulation of rainfall-induced landslides in the hillslopes of Guwahati, India C. Sarma et al. https://doi.org/10.1016/j.enggeo.2020.105523
109. Empirical and Physically Based Thresholds for the Occurrence of Shallow Landslides in a Prone Area of Northern Italian Apennines M. Bordoni et al. https://doi.org/10.3390/w11122653
110. Influence of soil mechanical and hydraulic parameters on the definition of rainfall intensity and duration thresholds based on Transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS) S. Zhang et al. https://doi.org/10.3389/feart.2022.971655
111. Effects of rainfall patterns on occurrence of shallow landslides: a case study in Futian town of Chongqing city J. He et al. https://doi.org/10.1080/19475705.2025.2568147
112. Multitemporal relative landslide exposure and risk analysis for the sustainable development of rapidly growing cities M. Di Napoli et al. https://doi.org/10.1007/s10346-023-02065-z
113. Flow-slide characteristics and failure mechanism of shallow landslides in granite residual soil under heavy rainfall H. Bai et al. https://doi.org/10.1007/s11629-022-7315-8
114. Including root reinforcement variability in a probabilistic 3D stability model A. Cislaghi et al. https://doi.org/10.1002/esp.4127
115. Numerical Analysis of 3D Slope Stability in a Rainfall-Induced Landslide: Insights from Different Hydrological Conditions and Soil Layering G. Chen et al. https://doi.org/10.3390/w17223316
116. Sensitivity analysis and calibration of a dynamic physically based slope stability model T. Zieher et al. https://doi.org/10.5194/nhess-17-971-2017
117. Hulu Kelang, Malaysia regional mapping of rainfall-induced landslides using TRIGRS model N. Saadatkhah et al. https://doi.org/10.1007/s12517-014-1410-2
118. Advanced Models Applied for the Elaboration of Landslide-Prone Maps, a Review T. König et al. https://doi.org/10.4236/ijg.2022.133010
119. Stability Prediction of Rainfall-Induced Shallow Landslides: A Case Study of Mountainous Area in China K. Song et al. https://doi.org/10.3390/w15162938
120. Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions A. Batar & T. Watanabe https://doi.org/10.3390/ijgi10030114
121. Evaluation of the influence of soil mechanical parameters on the position of rainfall thresholds for shallow landslides M. Velásquez & R. Marin https://doi.org/10.1007/s12517-022-10859-z
122. Constructing Rainfall Threshold for Debris Flows of a Defined Hazardous Magnitude Y. Li et al. https://doi.org/10.3390/rs16071265
123. Integrated prediction of rainfall-induced landslides using zone parameterization and physical-based probabilistic modelling R. Liu et al. https://doi.org/10.1016/j.geomorph.2026.110463
124. Spatial variation of shear strength properties incorporating auxiliary variables S. Ip et al. https://doi.org/10.1016/j.catena.2021.105196
125. Application of coupling physics–based model TRIGRS with random forest in rainfall-induced landslide-susceptibility assessment L. Yang et al. https://doi.org/10.1007/s10346-024-02276-y
126. Building risk amplification effect under loess landslides-hydraulic erosion-debris flow cascade in China Y. Chen et al. https://doi.org/10.1016/j.ijdrr.2024.105061
127. Hydrological factors affecting rainfall-induced shallow landslides: From the field monitoring to a simplified slope stability analysis M. Bordoni et al. https://doi.org/10.1016/j.enggeo.2015.04.006
128. Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China S. Ma et al. https://doi.org/10.3390/rs14174257
129. Development of a Landslide Early Warning System in Indonesia R. Hidayat et al. https://doi.org/10.3390/geosciences9100451
130. Quantification of climate change impact on rainfall-induced shallow landslide susceptibility: a case study in central Norway E. Oguz et al. https://doi.org/10.1080/17499518.2023.2283848
131. Application of analytic hierarchy process (AHP) model for landslide susceptibility mapping in the Nouvelle Matmata region, southern Tunisia H. Bensalem et al. https://doi.org/10.1016/j.jafrearsci.2026.106237
132. TRIGRS-FOSM: probabilistic slope stability tool for rainfall-induced landslide susceptibility assessment A. Ajith & R. Pillai https://doi.org/10.1007/s11069-024-06933-2