83 citations as recorded by crossref.

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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
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38. 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
39. 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
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43. 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
44. 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
45. 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
46. Physically-based modelling of granular flows with Open Source GIS M. Mergili et al. https://doi.org/10.5194/nhess-12-187-2012
47. 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
48. 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
49. 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
50. Debris-Flow and Debris-Flood Susceptibility Mapping for Geohazard Risk Prioritization M. Sturzenegger et al. https://doi.org/10.2113/EEG-D-20-00006
51. 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
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53. 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
54. 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
55. 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
56. 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
57. 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
58. 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
59. Regional rainfall thresholds for landslide occurrence using a centenary database T. Vaz et al. https://doi.org/10.5194/nhess-18-1037-2018
60. 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
61. 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
62. 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
63. 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
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65. 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
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70. 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
71. Identification of landslide hazard and risk ‘hotspots’ in Europe C. Jaedicke et al. https://doi.org/10.1007/s10064-013-0541-0
72. 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
73. 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
74. 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
75. 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
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77. 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
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