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
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- An Experimental Research on the Use of Recurrent Neural Networks in Landslide Susceptibility Mapping B. Mutlu et al. https://doi.org/10.3390/ijgi8120578
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- 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
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- 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
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