Articles | Volume 14, issue 9
https://doi.org/10.5194/nhess-14-2605-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-14-2605-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
29 Sep 2014
Research article |
| 29 Sep 2014
Bayesian network learning for natural hazard analyses
Institute of Earth and Environmental Sciences, University of Potsdam, Germany
Invited contribution by K. Vogel, recipient of the Outstanding Student Poster (OSP) Award 2012.
C. Riggelsen
Pivotal Software Inc., Palo Alto, USA
O. Korup
Institute of Earth and Environmental Sciences, University of Potsdam, Germany
F. Scherbaum
Institute of Earth and Environmental Sciences, University of Potsdam, Germany
Viewed
Total article views: 5,443 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Oct 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,651 | 2,595 | 197 | 5,443 | 195 | 216 |
- HTML: 2,651
- PDF: 2,595
- XML: 197
- Total: 5,443
- BibTeX: 195
- EndNote: 216
Total article views: 4,374 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 29 Sep 2014)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,958 | 2,247 | 169 | 4,374 | 182 | 207 |
- HTML: 1,958
- PDF: 2,247
- XML: 169
- Total: 4,374
- BibTeX: 182
- EndNote: 207
Total article views: 1,069 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 22 Oct 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 693 | 348 | 28 | 1,069 | 13 | 9 |
- HTML: 693
- PDF: 348
- XML: 28
- Total: 1,069
- BibTeX: 13
- EndNote: 9
Cited
42 citations as recorded by crossref.
- An Experimental Research on the Use of Recurrent Neural Networks in Landslide Susceptibility Mapping B. Mutlu et al. https://doi.org/10.3390/ijgi8120578
- Improving flood impact estimations T. Sieg & A. Thieken https://doi.org/10.1088/1748-9326/ac6d6c
- Meta-water-modelling (Meta-WaM): A new framework for increasing applicability of digital water modelling J. Molina et al. https://doi.org/10.1016/j.knosys.2025.113543
- Assessing hurricane-induced flood risks in urban areas using Bayesian networks and GIS applications D. Feng et al. https://doi.org/10.1007/s11069-025-07832-w
- A Comparison of Factors Driving Flood Losses in Households Affected by Different Flood Types G. Mohor et al. https://doi.org/10.1029/2019WR025943
- Ecosystem-based disaster risk reduction in mountains C. Moos et al. https://doi.org/10.1016/j.earscirev.2017.12.011
- 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
- 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
- Urban flood disaster risk evaluation based on ontology and Bayesian Network Z. Wu et al. https://doi.org/10.1016/j.jhydrol.2020.124596
- A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale: An Application to the Houston Ship Channel, Texas A. Couasnon et al. https://doi.org/10.3390/w10091190
- Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges E. Ragno et al. https://doi.org/10.5194/hess-26-1695-2022
- Probabilistic Seismic Hazard Analysis for China Based on Bayesian Network C. Liu & D. Lu https://doi.org/10.1785/0220230159
- Proposed Bayesian Network Framework to Model Multisite Seismic Hazard with Existing Probabilistic Seismic Hazard Analysis Results E. Gibson & M. Bensi https://doi.org/10.1061/AJRUA6.RUENG-1252
- Analysis of spatio-temporal dependence of inflow time series through Bayesian causal modelling H. Macian-Sorribes et al. https://doi.org/10.1016/j.jhydrol.2020.125722
- Application of statistical techniques to proportional loss data: Evaluating the predictive accuracy of physical vulnerability to hazardous hydro-meteorological events C. Chow et al. https://doi.org/10.1016/j.jenvman.2019.05.084
- A probabilistic model of decision making regarding the use of chemical dispersants to combat oil spills in the German Bight Z. Liu & U. Callies https://doi.org/10.1016/j.watres.2019.115196
- A multi‐perspective process safety risk assessment with hybrid risks T. Vafaee & M. Monfared https://doi.org/10.1111/risa.70028
- Bayesian Network Modeling for Risk-Based Water Quality Decisions with Sparse Data: Case Study of the Kiso River O. Mohamed & N. Hirayama https://doi.org/10.3390/pr13113636
- Flood vulnerability and risk assessment of urban traditional buildings in a heritage district of Kuala Lumpur, Malaysia D. D'Ayala et al. https://doi.org/10.5194/nhess-20-2221-2020
- Incorporating Uncertainty of the System Behavior in Flood Risk Assessment—Sava River Case Study T. Kekez et al. https://doi.org/10.3390/w12102676
- Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network S. Huang et al. https://doi.org/10.3390/land10020210
- Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models K. Vogel et al. https://doi.org/10.1029/2018WR022858
- Assessment of the predictability of inflow to reservoirs through Bayesian causality S. Zazo et al. https://doi.org/10.1080/02626667.2023.2200143
- Bayesian structural decomposition of streamflow time series V. Recacho & M. Laurini https://doi.org/10.1016/j.jhydrol.2024.132478
- Using Bayesian networks for the assessment of underwater scour for road and railway bridges A. Maroni et al. https://doi.org/10.1177/1475921720956579
- Performance assessment of Bayesian Causal Modelling for runoff temporal behaviour through a novel stability framework S. Zazo et al. https://doi.org/10.1016/j.jhydrol.2022.127832
- 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
- Assessment and prediction of ecological risk caused by anthropogenic activities during the implementation of China's ecological protection redline policy: A case study from the Yangtze River Delta J. Liu et al. https://doi.org/10.1016/j.jclepro.2025.146962
- 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
- BN-FLEMOΔ: a Bayesian-network-based flood loss estimation model for adaptation planning in Ho Chi Minh City, Vietnam K. Shahi et al. https://doi.org/10.5194/nhess-25-2845-2025
- Quantifying Flood Vulnerability Reduction via Private Precaution N. Sairam et al. https://doi.org/10.1029/2018EF000994
- Assessing urban flood disaster risk using Bayesian network model and GIS applications Z. Wu et al. https://doi.org/10.1080/19475705.2019.1685010
- Implementation of a Combined Fuzzy Controller Model to Enhance Risk Assessment in Oil and Gas Construction Projects M. Al-Mhdawi et al. https://doi.org/10.1109/ACCESS.2024.3399129
- Evaluation and attribution of flood disaster loss risk based on Bayesian network structure learning: a case study of the urban agglomeration in the middle Yangtze River Y. Zhixiang et al. https://doi.org/10.1007/s11069-026-08283-7
- The relative importance of driving factors of wildfire occurrence across climatic gradients in the Inner Mongolia, China H. Sun et al. https://doi.org/10.1016/j.ecolind.2021.108249
- 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
- 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
- Bayesian geomorphology O. Korup https://doi.org/10.1002/esp.4995
- Bayesian network-based spatial predictive modelling reveals COVID-19 transmission dynamics in Eswatini W. Dlamini et al. https://doi.org/10.1007/s41324-021-00421-6
- Risk Assessment of An Earthquake-Collapse-Landslide Disaster Chain by Bayesian Network and Newmark Models L. Han et al. https://doi.org/10.3390/ijerph16183330
- 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
- An integrated Bayesian networks and Geographic information system (BNs-GIS) approach for flood disaster risk assessment: A case study of Yinchuan, China Y. Lu et al. https://doi.org/10.1016/j.ecolind.2024.112322
42 citations as recorded by crossref.
- An Experimental Research on the Use of Recurrent Neural Networks in Landslide Susceptibility Mapping B. Mutlu et al. https://doi.org/10.3390/ijgi8120578
- Improving flood impact estimations T. Sieg & A. Thieken https://doi.org/10.1088/1748-9326/ac6d6c
- Meta-water-modelling (Meta-WaM): A new framework for increasing applicability of digital water modelling J. Molina et al. https://doi.org/10.1016/j.knosys.2025.113543
- Assessing hurricane-induced flood risks in urban areas using Bayesian networks and GIS applications D. Feng et al. https://doi.org/10.1007/s11069-025-07832-w
- A Comparison of Factors Driving Flood Losses in Households Affected by Different Flood Types G. Mohor et al. https://doi.org/10.1029/2019WR025943
- Ecosystem-based disaster risk reduction in mountains C. Moos et al. https://doi.org/10.1016/j.earscirev.2017.12.011
- 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
- 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
- Urban flood disaster risk evaluation based on ontology and Bayesian Network Z. Wu et al. https://doi.org/10.1016/j.jhydrol.2020.124596
- A Copula-Based Bayesian Network for Modeling Compound Flood Hazard from Riverine and Coastal Interactions at the Catchment Scale: An Application to the Houston Ship Channel, Texas A. Couasnon et al. https://doi.org/10.3390/w10091190
- Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges E. Ragno et al. https://doi.org/10.5194/hess-26-1695-2022
- Probabilistic Seismic Hazard Analysis for China Based on Bayesian Network C. Liu & D. Lu https://doi.org/10.1785/0220230159
- Proposed Bayesian Network Framework to Model Multisite Seismic Hazard with Existing Probabilistic Seismic Hazard Analysis Results E. Gibson & M. Bensi https://doi.org/10.1061/AJRUA6.RUENG-1252
- Analysis of spatio-temporal dependence of inflow time series through Bayesian causal modelling H. Macian-Sorribes et al. https://doi.org/10.1016/j.jhydrol.2020.125722
- Application of statistical techniques to proportional loss data: Evaluating the predictive accuracy of physical vulnerability to hazardous hydro-meteorological events C. Chow et al. https://doi.org/10.1016/j.jenvman.2019.05.084
- A probabilistic model of decision making regarding the use of chemical dispersants to combat oil spills in the German Bight Z. Liu & U. Callies https://doi.org/10.1016/j.watres.2019.115196
- A multi‐perspective process safety risk assessment with hybrid risks T. Vafaee & M. Monfared https://doi.org/10.1111/risa.70028
- Bayesian Network Modeling for Risk-Based Water Quality Decisions with Sparse Data: Case Study of the Kiso River O. Mohamed & N. Hirayama https://doi.org/10.3390/pr13113636
- Flood vulnerability and risk assessment of urban traditional buildings in a heritage district of Kuala Lumpur, Malaysia D. D'Ayala et al. https://doi.org/10.5194/nhess-20-2221-2020
- Incorporating Uncertainty of the System Behavior in Flood Risk Assessment—Sava River Case Study T. Kekez et al. https://doi.org/10.3390/w12102676
- Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network S. Huang et al. https://doi.org/10.3390/land10020210
- Identifying Driving Factors in Flood‐Damaging Processes Using Graphical Models K. Vogel et al. https://doi.org/10.1029/2018WR022858
- Assessment of the predictability of inflow to reservoirs through Bayesian causality S. Zazo et al. https://doi.org/10.1080/02626667.2023.2200143
- Bayesian structural decomposition of streamflow time series V. Recacho & M. Laurini https://doi.org/10.1016/j.jhydrol.2024.132478
- Using Bayesian networks for the assessment of underwater scour for road and railway bridges A. Maroni et al. https://doi.org/10.1177/1475921720956579
- Performance assessment of Bayesian Causal Modelling for runoff temporal behaviour through a novel stability framework S. Zazo et al. https://doi.org/10.1016/j.jhydrol.2022.127832
- 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
- Assessment and prediction of ecological risk caused by anthropogenic activities during the implementation of China's ecological protection redline policy: A case study from the Yangtze River Delta J. Liu et al. https://doi.org/10.1016/j.jclepro.2025.146962
- 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
- BN-FLEMOΔ: a Bayesian-network-based flood loss estimation model for adaptation planning in Ho Chi Minh City, Vietnam K. Shahi et al. https://doi.org/10.5194/nhess-25-2845-2025
- Quantifying Flood Vulnerability Reduction via Private Precaution N. Sairam et al. https://doi.org/10.1029/2018EF000994
- Assessing urban flood disaster risk using Bayesian network model and GIS applications Z. Wu et al. https://doi.org/10.1080/19475705.2019.1685010
- Implementation of a Combined Fuzzy Controller Model to Enhance Risk Assessment in Oil and Gas Construction Projects M. Al-Mhdawi et al. https://doi.org/10.1109/ACCESS.2024.3399129
- Evaluation and attribution of flood disaster loss risk based on Bayesian network structure learning: a case study of the urban agglomeration in the middle Yangtze River Y. Zhixiang et al. https://doi.org/10.1007/s11069-026-08283-7
- The relative importance of driving factors of wildfire occurrence across climatic gradients in the Inner Mongolia, China H. Sun et al. https://doi.org/10.1016/j.ecolind.2021.108249
- 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
- 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
- Bayesian geomorphology O. Korup https://doi.org/10.1002/esp.4995
- Bayesian network-based spatial predictive modelling reveals COVID-19 transmission dynamics in Eswatini W. Dlamini et al. https://doi.org/10.1007/s41324-021-00421-6
- Risk Assessment of An Earthquake-Collapse-Landslide Disaster Chain by Bayesian Network and Newmark Models L. Han et al. https://doi.org/10.3390/ijerph16183330
- 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
- An integrated Bayesian networks and Geographic information system (BNs-GIS) approach for flood disaster risk assessment: A case study of Yinchuan, China Y. Lu et al. https://doi.org/10.1016/j.ecolind.2024.112322
Saved (final revised paper)
Latest update: 26 Jul 2026
Altmetrics
Final-revised paper
Preprint