82 citations as recorded by crossref.

1. The role of multi-resolution DEMs and sampling strategy uncertainty in deep learning-based debris flow susceptibility mapping H. Daud et al. https://doi.org/10.1007/s11440-025-02841-2
2. Predicting the Initiation Area of a Debris Flow Using Geomorphological Characteristics and a Physically-Based Model C. Song et al. https://doi.org/10.9798/KOSHAM.2021.21.2.137
3. Debris Flow Assessment in the Gaizi-Bulunkou Section of Karakoram Highway N. Jiang et al. https://doi.org/10.3389/feart.2021.660579
4. Assessing residential buildings vulnerability on a regional scale to rainfall–earthquake-induced landslides on Mt. Umyeon, Korea B. Nguyen & T. Nguyen https://doi.org/10.1007/s11069-025-07824-w
5. Quantifying human risk from rainfall‐induced landslides across different return periods: A case study of Ba To, Quang Ngai, Vietnam B. Nguyen & T. Nguyen https://doi.org/10.1002/esp.70210
6. The MultiRISK platform: The technical concept and application of a regional-scale multihazard exposure analysis tool M. Kappes et al. https://doi.org/10.1016/j.geomorph.2012.01.024
7. Geohazard vulnerability assessment in Qiaojia seismic zones, SW China Z. Gao et al. https://doi.org/10.1016/j.ijdrr.2020.101928
8. La simulation comme outil d’anticipation des crues rapides dans les petits bassins versants en Seine-Maritime J. Douvinet et al. https://doi.org/10.3166/RIG.25.99-122
9. A Novel Multi-Risk Assessment Web-Tool for Evaluating Future Impacts of Global Change in Mountainous Areas G. Grandjean et al. https://doi.org/10.3390/cli6040092
10. Cartographie régionale de la susceptibilité aux laves torrentielles dans les Alpes du Sud M. Bertrand et al. https://doi.org/10.4000/rga.3530
11. A landscape scale model to predict post-fire debris flow impact zones T. Keeble et al. https://doi.org/10.1016/j.geomorph.2024.109175
12. Addressing accuracy challenges in machine learning for debris flow susceptibility: Insights from the Yalong River basin Z. Ming et al. https://doi.org/10.1007/s11629-024-9316-2
13. 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
14. 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
15. Estimating the Future Function of the Nipsa Reservoir due to Climate Change and Debris Sediment Factors F. Maris et al. https://doi.org/10.3390/cli7060076
16. Flash Flood Hazard Assessment in Small Agricultural Basins Coupling GIS-Data and Cellular Automata Modelling J. Douvinet https://doi.org/10.4018/ijaeis.2014010104
17. Robustness evaluation of the probability-based HTCA model for simulating debris-flow run-out extent: Case study of the 2010 Hongchun event, China Y. Ma et al. https://doi.org/10.1016/j.enggeo.2022.106918
18. Simulation of event-based landslides and debris flows at watershed level S. Chiang et al. https://doi.org/10.1016/j.geomorph.2011.09.016
19. Multi-hazard fragility analysis of a single-span prefabricated concrete frame structure under sequential earthquake and blast loading C. Zhao et al. https://doi.org/10.1007/s10518-026-02494-w
20. ASCHFLOW - A dynamic landslide run-out model for medium scale hazard analysis B. Quan Luna et al. https://doi.org/10.1186/s40677-016-0064-7
21. Regional Scale Mapping of Debris-Flow Susceptibility in the Southern French Alps M. Bertrand et al. https://doi.org/10.4000/rga.3543
22. Landslide consequence analysis: a region-scale indicator-based methodology A. Puissant et al. https://doi.org/10.1007/s10346-013-0429-x
23. A statistical framework for tree‐ring reconstruction of high‐magnitude mass movements: case study of snow avalanches in eastern canada D. Germain https://doi.org/10.1111/geoa.12138
24. Debris Flow Susceptibility Assessment in the Wudongde Dam Area, China Based on Rock Engineering System and Fuzzy C-Means Algorithm Y. Li et al. https://doi.org/10.3390/w9090669
25. Estimating the topographic predictability of debris flows N. Meyer et al. https://doi.org/10.1016/j.geomorph.2013.10.030
26. Tree-Based Machine Learning and Flow Simulation for Debris Flow Susceptibility, Runout Propagation, and Dynamics in the Higher Himalayas H. Daud et al. https://doi.org/10.1007/s11004-025-10196-3
27. Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway F. Qing et al. https://doi.org/10.3390/rs12182933
28. Material sources supplying debris flows in Jiangjia Gully T. Xiafei et al. https://doi.org/10.1007/s12665-020-09020-4
29. Risk Assessment of Debris Flow at Regional Scale considering Catchment Area in Chuncheon, Korea G. Choi et al. https://doi.org/10.1007/s12205-021-0136-4
30. Parameter Analysis of Flow-R Model for Physical Vulnerability Assessment of Debris Flow Disaster in Regional Scale H. Kang & Y. Kim https://doi.org/10.9798/KOSHAM.2017.17.4.233
31. Mapping debris flow susceptibility based on watershed unit and grid cell unit: a comparison study S. Qin et al. https://doi.org/10.1080/19475705.2019.1604572
32. Susceptibility assessment of debris flow from Baoxing River basin in Lushan earthquake zone, China L. Liu et al. https://doi.org/10.1088/1755-1315/861/5/052010
33. Analysis and quantification of potential consequences in multirisk coastal context at different spatial scales (Normandy, France) K. Graff et al. https://doi.org/10.1007/s11069-019-03763-5
34. Temporal Characteristics of Debris Flow Surges J. Zhang et al. https://doi.org/10.3389/feart.2021.660655
35. Flow-R, a model for susceptibility mapping of debris flows and other gravitational hazards at a regional scale P. Horton et al. https://doi.org/10.5194/nhess-13-869-2013
36. Regional vulnerability assessment for debris flows in China—a CWS approach M. Ding et al. https://doi.org/10.1007/s10346-015-0578-1
37. Integrating expert opinion with modelling for quantitative multi-hazard risk assessment in the Eastern Italian Alps L. Chen et al. https://doi.org/10.1016/j.geomorph.2016.07.041
38. Debris flow susceptibility assessment of Leh Valley, Ladakh, based on concepts of connectivity, propagation and evidence-based probability C. Sharma et al. https://doi.org/10.1007/s11069-022-05619-x
39. Spatial differentiation and risk zonation of debris flow hazards in Tajikistan W. JIA et al. https://doi.org/10.1016/j.regsus.2026.100299
40. Assessing Debris-Flow Susceptibility at Local and Global Scales: A Deep-Learning-Based Comparative Study ofSichuan, China, and Worldwide A. Nienkötter et al. https://doi.org/10.3390/rs18091442
41. GIS-based numerical modelling of debris flow motion across three-dimensional terrain J. Wu et al. https://doi.org/10.1007/s11629-013-2486-y
42. Dynamic hazard assessment of debris flow based on TRIGRS and flow-R coupled models Y. Nie et al. https://doi.org/10.1007/s00477-021-02093-y
43. Regional debris flow susceptibility analysis in mountainous peri-urban areas through morphometric and land cover indicators M. Rogelis & M. Werner https://doi.org/10.5194/nhess-14-3043-2014
44. 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
45. Physically-based modelling of granular flows with Open Source GIS M. Mergili et al. https://doi.org/10.5194/nhess-12-187-2012
46. A multivariate statistical method for susceptibility analysis of debris flow in southwestern China F. Ji et al. https://doi.org/10.5194/nhess-20-1321-2020
47. Debris Flow Susceptibility Evaluation in Meizoseismal Region: A Case Study in Jiuzhaigou, China Y. Li et al. https://doi.org/10.1007/s12583-022-1803-1
48. Susceptibility assessment of soil-water hazard chain on a small catchment in gully region of Loess Plateau: Implications for artificially-induced mountaintop removal filling valley and geoheritage H. Shu & F. Zhang https://doi.org/10.1016/j.geomorph.2023.108949
49. Debris-Flow and Debris-Flood Susceptibility Mapping for Geohazard Risk Prioritization M. Sturzenegger et al. https://doi.org/10.2113/EEG-D-20-00006
50. 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
51. Characterization of elements at risk in the multirisk coastal context and at different spatial scales: Multi-database integration (normandy, France). K. Graff et al. https://doi.org/10.1016/j.apgeog.2019.102076
52. Assessment of exposed elements in a changing built environment by using an integrated model of debris flow initiation and runout (Kalimpong region, Himalaya) S. Bera et al. https://doi.org/10.1007/s10064-021-02352-w
53. Identification and characterization of rainfall events responsible for triggering of debris flows and shallow landslides C. Iadanza et al. https://doi.org/10.1016/j.jhydrol.2016.01.018
54. Parameterization of a numerical 2-D debris flow model with entrainment: a case study of the Faucon catchment, Southern French Alps H. Hussin et al. https://doi.org/10.5194/nhess-12-3075-2012
55. Modelling post‐earthquake cascading hazards: Changing patterns of landslide runout following the 2015 Gorkha earthquake, Nepal M. Kincey et al. https://doi.org/10.1002/esp.5501
56. Debris flow susceptibility assessment based on an empirical approach in the central region of South Korea S. Kang & S. Lee https://doi.org/10.1016/j.geomorph.2018.01.025
57. Debris flow susceptibility and propagation modeling: a deep learning and flow-R framework M. Khalid et al. https://doi.org/10.1007/s10064-025-04496-5
58. Regional rainfall thresholds for landslide occurrence using a centenary database T. Vaz et al. https://doi.org/10.5194/nhess-18-1037-2018
59. Groundwater Overexploitation Causing Land Subsidence: Hazard Risk Assessment Using Field Observation and Spatial Modelling B. Huang et al. https://doi.org/10.1007/s11269-012-0141-y
60. Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multi-criteria M. Rahman et al. https://doi.org/10.1007/s11629-016-4220-z
61. Modeling debris flow initiation and run-out in recently burned areas using data-driven methods R. Melo & J. Zêzere https://doi.org/10.1007/s11069-017-2921-4
62. Combining stratigraphic forward modeling and susceptibility mapping to investigate the origin and evolution of submarine canyons L. Wan et al. https://doi.org/10.1016/j.geomorph.2021.108047
63. Quantitative Assessment of Regional Debris-Flow Risk: A Case Study in Southwest China G. Liu et al. https://doi.org/10.3390/su10072223
64. Morphometry and Debris-Flow Susceptibility Map in Mountain Drainage Basins of the Vallo di Diano, Southern Italy S. Giano et al. https://doi.org/10.3390/rs13163254
65. Hydrodynamic and topography based cellular automaton model for simulating debris flow run-out extent and entrainment behavior Z. Han et al. https://doi.org/10.1016/j.watres.2021.116872
66. Exploring best-fit parameters for propagation modeling of shallow landslides, using Flow-R J. Palacio et al. https://doi.org/10.1016/j.jsames.2025.105739
67. Assessing Susceptibility of Debris Flow in Southwest China Using Gradient Boosting Machine B. Di et al. https://doi.org/10.1038/s41598-019-48986-5
68. Establishing a GIS-based evaluation method considering spatial heterogeneity for debris flow susceptibility mapping at the regional scale S. Qin et al. https://doi.org/10.1007/s11069-022-05487-5
69. Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: application to western Southern Alps of New Zealand T. Kritikos & T. Davies https://doi.org/10.1007/s10346-014-0533-6
70. Identification of landslide hazard and risk ‘hotspots’ in Europe C. Jaedicke et al. https://doi.org/10.1007/s10064-013-0541-0
71. Debris-Flow Susceptibility Assessment in China: A Comparison between Traditional Statistical and Machine Learning Methods H. Huang et al. https://doi.org/10.3390/rs14184475
72. Semi-quantitative method to identify the vulnerable areas in terms of building aggregation for probable landslide runout at the regional scale: a case study from Soacha Province, Colombia A. Pradhan et al. https://doi.org/10.1007/s10064-019-01533-y
73. A flash flood hazard assessment in dry valleys (northern France) by cellular automata modelling J. Douvinet et al. https://doi.org/10.1007/s11069-014-1470-3
74. Regional-scale landslide risk assessment on Mt. Umyeon using risk index estimation B. Nguyen & Y. Kim https://doi.org/10.1007/s10346-021-01622-8
75. Integrating machine learning and deep learning for enhanced landslide susceptibility and propagation dynamics in Northwestern Pakistan H. Ullah et al. https://doi.org/10.1007/s10064-026-04940-0
76. Debris flow susceptibility mapping in alpine canyon region: a case study of Nujiang Prefecture Y. Li et al. https://doi.org/10.1007/s10064-024-03657-2
77. Modeling the flow behavior of a simulated municipal solid waste Z. Dai et al. https://doi.org/10.1007/s10064-015-0735-8
78. Debris flows modeling using geo-environmental factors: developing hybridized deep-learning algorithms Y. Li et al. https://doi.org/10.1080/10106049.2021.1912194
79. Comparison of Conventional Deterministic and Entropy-Based Methods for Predicting Sediment Concentration in Debris Flow Z. Zhu et al. https://doi.org/10.3390/w11030439
80. Stochastic generation of multi-site daily precipitation for applications in risk management K. Breinl et al. https://doi.org/10.1016/j.jhydrol.2013.06.015
81. On the criteria to create a susceptibility map to debris flow at a regional scale using Flow-R R. Pastorello et al. https://doi.org/10.1007/s11629-016-4077-1
82. Correlation between the rainfall, sediment recharge, and triggering of torrential flows in the Rebaixader catchment (Pyrenees, Spain) R. Pastorello et al. https://doi.org/10.1007/s10346-018-1000-6