60 citations as recorded by crossref.

1. Susceptibility to shallow landslides in a drainage basin in the Serra do Mar, São Paulo, Brazil, predicted using the SINMAP mathematical model T. Nery & B. Vieira https://doi.org/10.1007/s10064-014-0622-8
2. A multi-step hazard assessment for debris-flow prone areas influenced by hydroclimatic events V. Cabral et al. https://doi.org/10.1016/j.enggeo.2022.106961
3. Landslide prediction, monitoring and early warning: a concise review of state-of-the-art B. Chae et al. https://doi.org/10.1007/s12303-017-0034-4
4. Modeling the contribution of trees to shallow landslide development in a steep, forested watershed D. Kim et al. https://doi.org/10.1016/j.ecoleng.2013.05.003
5. Landslide inventory of the 2023 Serra do Mar disaster (Brazil) J. Bonini et al. https://doi.org/10.1007/s44288-025-00153-2
6. A Geotechnical–Hydrogeological Property Zonation Approach for Landslide Hazard Modelling in the eThekwini Metropolitan Region, Eastern South Africa S. Chiliza et al. https://doi.org/10.3390/geohazards7040110
7. 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
8. Assessing limit equilibrium method approach and mapping critical areas for slope stability analysis in Serra do Mar Paranaense—Brazil A. Acevedo et al. https://doi.org/10.1007/s12665-021-09863-5
9. Influence Factors Involving Rainfall-Induced Shallow Slope Failure: Numerical Study S. Yubonchit et al. https://doi.org/10.1061/(ASCE)GM.1943-5622.0000865
10. A comparison of statistical and deterministic methods for shallow landslide susceptibility zoning in clayey soils M. Ciurleo et al. https://doi.org/10.1016/j.enggeo.2017.04.023
11. A comparative evaluation of supervised machine learning algorithms for township level landslide susceptibility zonation in parts of Indian Himalayas B. Peethambaran et al. https://doi.org/10.1016/j.catena.2020.104751
12. Early warning models for rainfall-triggered clustered landslides: Advances, challenges and future perspectives C. Xie & C. Xu https://doi.org/10.1016/j.gespch.2026.100030
13. Development of time-variant landslide-prediction software considering three-dimensional subsurface unsaturated flow H. An et al. https://doi.org/10.1016/j.envsoft.2016.08.009
14. Assessment of landslide occurrences in Serra do Mar mountain range using kinematic analyses R. Cerri et al. https://doi.org/10.1007/s12665-018-7508-1
15. Warning Model for Shallow Landslides Induced by Extreme Rainfall L. Chien et al. https://doi.org/10.3390/w7084362
16. Landslide Susceptibility Mapping in Brazil: A Review H. Dias et al. https://doi.org/10.3390/geosciences11100425
17. Preliminary studies on the dynamic prediction method of rainfall-triggered landslide Y. Chen et al. https://doi.org/10.1007/s11629-014-3110-5
18. 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
19. 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
20. 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
21. Socio-geoenvironmental vulnerability index (SGeoVI) derived from hybrid modeling related to populations at-risk to landslides F. de Ávila et al. https://doi.org/10.1007/s11069-024-06526-z
22. 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
23. A hybrid model to overcome landslide inventory incompleteness issue for landslide susceptibility prediction J. Tan et al. https://doi.org/10.1080/10106049.2024.2322066
24. Developing an Accessible Landslide Susceptibility Model Using Open-Source Resources K. An et al. https://doi.org/10.3390/su10020293
25. Deterministic approach for susceptibility assessment of shallow debris slide in the Darjeeling Himalayas, India S. Sarkar et al. https://doi.org/10.1016/j.catena.2016.02.009
26. A review of recent studies on landslide hazard in Latin America A. Valdés Carrera et al. https://doi.org/10.1080/02723646.2021.1978372
27. Landslide and debris flow susceptibility zonation using TRIGRS for the 2011 Seoul landslide event D. Park et al. https://doi.org/10.5194/nhess-13-2833-2013
28. 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
29. Feasibility assessment of multi-criteria decision making and quantitative landslide susceptibility methods: A case study of Mao-Maram Manipur K. Khusulio & R. Kumar https://doi.org/10.1007/s12040-023-02062-9
30. Identifying hydrological pre-conditions and rainfall triggers of slope failures at catchment scale for 2014 storm events in the Ialomita Subcarpathians, Romania Z. Chitu et al. https://doi.org/10.1007/s10346-016-0740-4
31. Landslide susceptibility mapping using the infinite slope, SHALSTAB, SINMAP, and TRIGRS models in Serra do Mar, Brazil T. do Pinho & O. Augusto Filho https://doi.org/10.1007/s11629-021-7057-z
32. 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
33. Landslide susceptibility zonation around Dharamshala, Himachal Pradesh, India: an artificial intelligence model–based assessment K. Sweta et al. https://doi.org/10.1007/s10064-022-02806-9
34. Assessing shallow landslide hazards using the TRIGRS and SHALSTAB models, Serra do Mar, Brazil B. Vieira et al. https://doi.org/10.1007/s12665-018-7436-0
35. The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models F. Ávila et al. https://doi.org/10.1007/s11069-020-04324-x
36. Shallow landslide prediction and analysis with risk assessment using a spatial model in a coastal region in the state of São Paulo, Brazil P. Camarinha et al. https://doi.org/10.5194/nhess-14-2449-2014
37. 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
38. Methodologies of Economic Measurement and Vulnerability Assessment for Application in Landslide Risk Analysis in a Highway Domain Strip: A Case Study in the Serra Pelada Region (Brazil) E. Felizardo Batista et al. https://doi.org/10.3390/su11216130
39. Physically based shallow translational landslide susceptibility analysis in Tibo catchment, NW of Portugal M. Teixeira et al. https://doi.org/10.1007/s10346-014-0494-9
40. Assessment of rainfall-induced shallow landslides in Phetchabun and Krabi provinces, Thailand K. Ono et al. https://doi.org/10.1007/s11069-014-1292-3
41. A prototype system for space–time assessment of rainfall-induced shallow landslides in Italy L. Montrasio et al. https://doi.org/10.1007/s11069-014-1239-8
42. Integrated debris flow hazard assessment in critical infrastructure zones: a case study of the 2022 Cubatão event, Serra do Mar, Brazil V. Veloso et al. https://doi.org/10.1007/s11069-026-07969-2
43. Modeling the geomorphologic susceptibility to debris flow initiation with geomorphometric parameters and fuzzy logic in the mountainous region of Serra do Mar (state of Paraná, Brazil) R. Silveira & C. Silveira https://doi.org/10.1007/s11069-025-07264-6
44. Mobility characteristics of debris slides and flows triggered by Hurricane Maria in Puerto Rico E. Bessette-Kirton et al. https://doi.org/10.1007/s10346-020-01445-z
45. Landslide and slope stability evaluation in the historical town of Kruja, Albania Y. Muceku & O. Korini https://doi.org/10.5194/nhess-14-545-2014
46. Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques Z. Ferreira et al. https://doi.org/10.1080/19475705.2025.2471019
47. Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast H. Dias et al. https://doi.org/10.1007/s11069-021-04676-y
48. Comparison between different approaches to modeling shallow landslide susceptibility: a case history in Oltrepo Pavese, Northern Italy D. Zizioli et al. https://doi.org/10.5194/nhess-13-559-2013
49. Rainfall-Induced Landslide Susceptibility Assessment and the Establishment of Early Warning Techniques at Regional Scale C. Hsu https://doi.org/10.3390/su152416764
50. Mapping Landslide Prediction through a GIS-Based Model: A Case Study in a Catchment in Southern Italy F. Moresi et al. https://doi.org/10.3390/geosciences10080309
51. Landslide susceptibility mapping in and around Mussoorie Township using fuzzy set procedure, MamLand and improved fuzzy expert system-A comparative study B. Peethambaran et al. https://doi.org/10.1007/s11069-018-3532-4
52. 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
53. 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
54. 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
55. Assessing the Effectiveness of TRIGRS for Predicting Unstable Areas in a Tropical Mountain Basin (Colombian Andes) R. Marin et al. https://doi.org/10.1007/s10706-020-01630-w
56. The dynamics of debris-flow events in Southeast Brazil V. Cabral et al. https://doi.org/10.1016/j.jsames.2025.105523
57. 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
58. 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
59. Landslide susceptibility assessment using GIS and the infinite slope model in Salvador, Brazil L. Silva et al. https://doi.org/10.1080/19475683.2026.2652260
60. Basinwide disaster loss assessments under extreme climate scenarios: a case study of the Kaoping River basin H. Li et al. https://doi.org/10.1007/s11069-016-2729-7