100 citations as recorded by crossref.

1. An assessment and identification of avalanche hazard sites in Uri sector and its surroundings on Himalayan mountain A. A. S. Mohammed et al. https://doi.org/10.1007/s11629-014-3274-z
2. Assessing physical vulnerability in large cities exposed to flash floods and debris flows: the case of Arequipa (Peru) J. Thouret et al. https://doi.org/10.1007/s11069-014-1172-x
3. Probabilistic prediction of wildfire economic losses to housing in Cyprus using Bayesian network analysis P. Papakosta et al. https://doi.org/10.1071/WF15113
4. Flood susceptibility assessment based on a novel random Naïve Bayes method: A comparison between different factor discretization methods X. Tang et al. https://doi.org/10.1016/j.catena.2020.104536
5. Snow avalanche disturbances in forest ecosystems—State of research and implications for management P. Bebi et al. https://doi.org/10.1016/j.foreco.2009.01.050
6. Prioritisation Recommendation Mapping (PrioReMap): A method for supporting relief coordination in flood disaster response M. Schneider et al. https://doi.org/10.1016/j.ijdrr.2025.105949
7. Probabilistic prediction of daily fire occurrence in the Mediterranean with readily available spatio-temporal data P. Papakosta & D. Straub https://doi.org/10.3832/ifor1686-009
8. Application of Bayesian networks for fire risk mapping using GIS and remote sensing data W. Dlamini https://doi.org/10.1007/s10708-010-9362-x
9. Investigating forest fire causes through an integrated Bayesian network and geographic information system approach Z. Konurhan et al. https://doi.org/10.1007/s11069-025-07304-1
10. Quantifying uncertainties in earth observation-based ecosystem service assessments A. Stritih et al. https://doi.org/10.1016/j.envsoft.2018.09.005
11. Addressing disturbance risk to mountain forest ecosystem services A. Stritih et al. https://doi.org/10.1016/j.jenvman.2021.113188
12. An elicitation process to quantify Bayesian networks for dam failure analysis A. Verzobio et al. https://doi.org/10.1139/cjce-2020-0089
13. Bayesian Network Based Probabilistic Modelling of Earthquake Induced Landslides T. Das & D. Choudhury https://doi.org/10.3208/jgssp.v10.OS-2-04
14. A software system for assessing the spatially distributed ecological risk posed by oil shipping A. Jolma et al. https://doi.org/10.1016/j.envsoft.2014.06.023
15. A Bayesian probabilistic framework for avalanche modelling based on observations D. Straub & A. Grêt-Regamey https://doi.org/10.1016/j.coldregions.2006.08.024
16. A Bayesian network model to disentangle the effects of stand and climate factors on tree mortality of Chinese fir plantations Y. Jiang et al. https://doi.org/10.3389/ffgc.2023.1298968
17. A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people S. Balbi et al. https://doi.org/10.5194/nhess-16-1323-2016
18. Bayesian optimal design of an avalanche dam using a multivariate numerical avalanche model N. Eckert et al. https://doi.org/10.1007/s00477-008-0287-6
19. Bayesian stochastic modelling for avalanche predetermination: from a general system framework to return period computations N. Eckert et al. https://doi.org/10.1007/s00477-007-0107-4
20. Dynamic process-based risk assessment of debris flow on a local scale Q. Zou et al. https://doi.org/10.1080/02723646.2016.1169477
21. Scenario Prediction of the Rainstorm-Collapse-Landslide-Debris Flow Disaster Chain Based on Bayesian Networks C. Dai et al. https://doi.org/10.1061/NHREFO.NHENG-2378
22. Integrating Entropy‐Based Naïve Bayes and GIS for Spatial Evaluation of Flood Hazard R. Liu et al. https://doi.org/10.1111/risa.12698
23. Dynamic Bayesian Network for Probabilistic Modeling of Tunnel Excavation Processes O. Špačková & D. Straub https://doi.org/10.1111/j.1467-8667.2012.00759.x
24. Linking GIS-based models to value ecosystem services in an Alpine region A. Grêt-Regamey et al. https://doi.org/10.1016/j.jenvman.2007.05.019
25. An online platform for spatial and iterative modelling with Bayesian Networks A. Stritih et al. https://doi.org/10.1016/j.envsoft.2020.104658
26. Spaceborne LiDAR for characterizing forest structure across scales in the European Alps L. Mandl et al. https://doi.org/10.1002/rse2.330
27. On the use of Bayesian networks as a meta-modelling approach to analyse uncertainties in slope stability analysis I. Chivatá Cárdenas https://doi.org/10.1080/17499518.2018.1498524
28. Risk assessment of simultaneous debris flows in mountain townships P. Cui et al. https://doi.org/10.1177/0309133313491445
29. Risk-based optimal inspection strategies for structural systems using dynamic Bayesian networks J. Luque & D. Straub https://doi.org/10.1016/j.strusafe.2018.08.002
30. Evaluating the benefit of avalanche protection forest with GIS-based risk analyses—A case study in Switzerland M. Teich & P. Bebi https://doi.org/10.1016/j.foreco.2009.01.046
31. Bayesian network learning for natural hazard analyses K. Vogel et al. https://doi.org/10.5194/nhess-14-2605-2014
32. Income vulnerability of rural households in Bangladesh: a comparison between Bayesian and classical methods M. Islam et al. https://doi.org/10.1080/00949655.2012.656310
33. Using Bayesian networks in analyzing powerful earthquake disaster chains J. Wang et al. https://doi.org/10.1007/s11069-013-0631-0
34. A real-world application of Monte Carlo procedure for debris flow risk assessment B. Calvo & F. Savi https://doi.org/10.1016/j.cageo.2008.04.002
35. Snow Avalanche Risk Assessment using GIS-Based AHP for Bitlis Province M. AYDIN et al. https://doi.org/10.17798/bitlisfen.1195607
36. Probabilistic Graphical Models for Flood State Detection of Roads Combining Imagery and DEM D. Frey et al. https://doi.org/10.1109/LGRS.2012.2188881
37. Valuing Ecosystem Services for Sustainable Landscape Planning in Alpine Regions A. Grêt-Regamey et al. https://doi.org/10.1659/mrd.0951
38. Encoding Dependence in Bayesian Causal Networks J. Sulik et al. https://doi.org/10.3389/fenvs.2016.00084
39. 3-D numerical approach to simulate the overtopping volume caused by an impulse wave comparable to avalanche impact in a reservoir R. Gabl et al. https://doi.org/10.5194/nhess-15-2617-2015
40. Bayesian networks in environmental modelling P. Aguilera et al. https://doi.org/10.1016/j.envsoft.2011.06.004
41. A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data A. D'Addabbo et al. https://doi.org/10.1109/TGRS.2016.2520487
42. Quantitative risk and optimal design approaches in the snow avalanche field: Review and extensions N. Eckert et al. https://doi.org/10.1016/j.coldregions.2012.03.003
43. Practical Approach for Reliability-Based Inspection Planning of Jacket Platforms Using Bayesian Networks H. Gholami et al. https://doi.org/10.1061/AJRUA6.0001071
44. Hazard Assessment of Earthquake Disaster Chains Based on a Bayesian Network Model and ArcGIS L. Han et al. https://doi.org/10.3390/ijgi8050210
45. Risk assessment of drinking water intake contamination from agricultural activities using a Bayesian network R. Kammoun et al. https://doi.org/10.1371/journal.pwat.0000073
46. A comparison of two sensitivity analysis techniques based on four bayesian models representing ecosystem services provision in the Argentine Pampas F. Rositano et al. https://doi.org/10.1016/j.ecoinf.2017.07.005
47. Reviewing Bayesian Networks potentials for climate change impacts assessment and management: A multi-risk perspective A. Sperotto et al. https://doi.org/10.1016/j.jenvman.2017.07.044
48. Envisioning uncertainty in geospatial information K. Laskey et al. https://doi.org/10.1016/j.ijar.2009.05.011
49. Validation of extreme snow avalanches and related return periods derived from a statistical-dynamical model using tree-ring techniques R. Schläppy et al. https://doi.org/10.1016/j.coldregions.2013.12.001
50. A dynamic evaluation method for slope safety with monitoring information based on a hybrid intelligence algorithm C. Wang et al. https://doi.org/10.1016/j.compgeo.2023.105772
51. A Framework for Assessing and Managing Risks Posed by Emerging Diseases T. Walshe & M. Burgman https://doi.org/10.1111/j.1539-6924.2009.01305.x
52. A HAZARD MAP BASED ON 3D SNOW AVALANCHE SIMULATION K. KITOU et al. https://doi.org/10.2208/jscejam.74.I_203
53. Analysis of human risks due to dam-break floods—part 1: a new model based on Bayesian networks M. Peng & L. Zhang https://doi.org/10.1007/s11069-012-0275-5
54. A spatially explicit decision support framework for parcel- and community-level resilience assessment using Bayesian networks D. Sanderson et al. https://doi.org/10.1080/23789689.2021.1966164
55. Integration of Bayesian Networks with GIS for Dynamic Avalanche Hazard Assessment: NSDI Perspective İ. Yılmaz & D. Öztürk https://doi.org/10.21324/dacd.365255
56. A Review of Bayesian Networks for Spatial Data C. Krapu et al. https://doi.org/10.1145/3516523
57. Risk management in civil engineering S. Mašović et al. https://doi.org/10.5937/GRMK2300003M
58. Spatial bayesian approach for socio-economic assessment of pumped hydro storage S. Ali et al. https://doi.org/10.1016/j.rser.2023.114007
59. Modeling land use decisions with Bayesian networks: Spatially explicit analysis of driving forces on land use change E. Celio et al. https://doi.org/10.1016/j.envsoft.2013.10.014
60. Multi-risk assessment in mountain regions: A review of modelling approaches for climate change adaptation S. Terzi et al. https://doi.org/10.1016/j.jenvman.2018.11.100
61. Avalanche protection by forests — A choice experiment in the Swiss Alps R. Olschewski et al. https://doi.org/10.1016/j.forpol.2012.02.016
62. Urban Infrastructure Vulnerability to Climate-Induced Risks: A Probabilistic Modeling Approach Using Remote Sensing as a Tool in Urban Planning I. Rodríguez-Antuñano et al. https://doi.org/10.3390/infrastructures9070107
63. Spatial Analysis of Climate Risk in the West Bank, Palestine S. Alawna & X. Garcia https://doi.org/10.3390/world6030121
64. Good practice in Bayesian network modelling S. Chen & C. Pollino https://doi.org/10.1016/j.envsoft.2012.03.012
65. An Optimized Weighted Naïve Bayes Method for Flood Risk Assessment X. Tang et al. https://doi.org/10.1111/risa.13743
66. Bayesian Network Enhanced with Structural Reliability Methods: Methodology D. Straub & A. Der Kiureghian https://doi.org/10.1061/(ASCE)EM.1943-7889.0000173
67. Slope safety evaluation by integrating multi-source monitoring information M. Peng et al. https://doi.org/10.1016/j.strusafe.2013.08.007
68. Using spatial analysis and Bayesian network to model the vulnerability and make insurance pricing of catastrophic risk L. Li et al. https://doi.org/10.1080/13658816.2010.510473
69. Assessing spatial likelihood of flooding hazard using naïve Bayes and GIS: a case study in Bowen Basin, Australia R. Liu et al. https://doi.org/10.1007/s00477-015-1198-y
70. Automatic Detection of Regional Snow Avalanches with Scattering and Interference of C-band SAR Data J. Yang et al. https://doi.org/10.3390/rs12172781
71. Assessment of Catastrophic Risk Using Bayesian Network Constructed from Domain Knowledge and Spatial Data L. Li et al. https://doi.org/10.1111/j.1539-6924.2010.01429.x
72. Methodischer Rahmen für den Einsatz von Backcasting zur Anpassung an den Klimawandel A. Grêt-Regamey & S. Brunner https://doi.org/10.1080/02513625.2011.10557123
73. Bayesian-network-based system identification of spatial distribution of structural parameters S. Lee & J. Song https://doi.org/10.1016/j.engstruct.2016.08.029
74. Ecosystem-based disaster risk reduction in mountains C. Moos et al. https://doi.org/10.1016/j.earscirev.2017.12.011
75. Improved Bayesian Network-Based Risk Model and Its Application in Disaster Risk Assessment M. Li et al. https://doi.org/10.1007/s13753-018-0171-z
76. Avalanche protection by forests — A choice experiment in the Swiss Alps R. Olschewski et al. https://doi.org/10.1016/j.forpol.2011.10.002
77. Towards a holistic paradigm for long-term snow avalanche risk assessment and mitigation N. Eckert & F. Giacona https://doi.org/10.1007/s13280-022-01804-1
78. Integrating Bayesian networks and geographic information systems: Good practice examples S. Johnson et al. https://doi.org/10.1002/ieam.262
79. Improving ecosystem services modelling: Insights from a Bayesian network tools review E. Pérez-Miñana https://doi.org/10.1016/j.envsoft.2016.07.007
80. A Bayesian network model for prediction and analysis of possible forest fire causes V. Sevinc et al. https://doi.org/10.1016/j.foreco.2019.117723
81. Facing uncertainty in ecosystem services-based resource management A. Grêt-Regamey et al. https://doi.org/10.1016/j.jenvman.2012.07.028
82. Bayesian network for characterizing model uncertainty of liquefaction potential evaluation models H. Huang et al. https://doi.org/10.1007/s12205-012-1367-1
83. Dynamic Process Analysis and Hazard Prediction of Debris Flow in Eastern Qinghai-Tibet Plateau Area—A Case Study at Ridi Gully Q. Zou et al. https://doi.org/10.1657/AAAR0017-019
84. A Framework for the Integration of Nature-Based Solutions into Environmental Risk Management Strategies C. Accastello et al. https://doi.org/10.3390/su11020489
85. Stochastic Modeling of Deterioration Processes through Dynamic Bayesian Networks D. Straub https://doi.org/10.1061/(ASCE)EM.1943-7889.0000024
86. Multi-hazard risk assessment for roads: probabilistic versus deterministic approaches S. Oberndorfer et al. https://doi.org/10.5194/nhess-20-3135-2020
87. Quantifying the influence of avalanche release volume on runout probability: A case study of a mountain road section S. Oberndorfer et al. https://doi.org/10.1016/j.coldregions.2025.104734
88. Estimating Leaf Area Index by Bayesian Linear Regression Using Terrestrial LiDAR, LAI-2200 Plant Canopy Analyzer, and Landsat TM Spectral Indices N. Ilangakoon et al. https://doi.org/10.1080/07038992.2015.1102629
89. Regional risk assessment of debris flows in China—An HRU-based approach Q. Zou et al. https://doi.org/10.1016/j.geomorph.2019.04.027
90. The Economy-Wide Value-at-Risk from the Exposure of Natural Capital to Climate Change and Extreme Natural Events: The Case of Wind Damage and Forest Recreational Services in New Zealand J. Monge & G. McDonald https://doi.org/10.1016/j.ecolecon.2020.106747
91. Bayesian network based probabilistic approach for uncertainty analysis of earthquake induced landslides T. Das & D. Choudhury https://doi.org/10.1016/j.sandf.2025.101620
92. A spatial assessment of urban waterlogging risk based on a Weighted Naïve Bayes classifier X. Tang et al. https://doi.org/10.1016/j.scitotenv.2018.02.172
93. Snow Avalanches in Forested Terrain: Influence of Forest Parameters, Topography, and Avalanche Characteristics on Runout Distance M. Teich et al. https://doi.org/10.1657/1938-4246-44.4.509
94. Reliability and effectiveness of early warning systems for natural hazards: Concept and application to debris flow warning M. Sättele et al. https://doi.org/10.1016/j.ress.2015.05.003
95. Comparative study of deterministic and probabilistic assessments of microbial risk associated with combined sewer overflows upstream of drinking water intakes R. Kammoun et al. https://doi.org/10.1016/j.envc.2023.100735
96. A new approach to assess landslide susceptibility based on slope failure mechanisms Q. Zou et al. https://doi.org/10.1016/j.catena.2021.105388
97. A review of graphical methods to map the natural hazard-to-wellbeing risk chain in a socio-ecological system J. Monge et al. https://doi.org/10.1016/j.scitotenv.2021.149947
98. A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping D. Landuyt et al. https://doi.org/10.1016/j.envsoft.2015.05.002
99. A model to predict ordinal suitability using sparse and uncertain data R. Whitsed et al. https://doi.org/10.1016/j.apgeog.2011.06.016
100. Landslide hazards mapping using uncertain Naïve Bayesian classification method Y. Mao et al. https://doi.org/10.1007/s11771-015-2891-1