Articles | Volume 14, issue 9
https://doi.org/10.5194/nhess-14-2605-2014
https://doi.org/10.5194/nhess-14-2605-2014
Research article
 | 
29 Sep 2014
Research article |  | 29 Sep 2014

Bayesian network learning for natural hazard analyses

K. Vogel, C. Riggelsen, O. Korup, and F. Scherbaum

Related authors

Damage assessment in Braunsbach 2016: data collection and analysis for an improved understanding of damaging processes during flash floods
Jonas Laudan, Viktor Rözer, Tobias Sieg, Kristin Vogel, and Annegret H. Thieken
Nat. Hazards Earth Syst. Sci., 17, 2163–2179, https://doi.org/10.5194/nhess-17-2163-2017,https://doi.org/10.5194/nhess-17-2163-2017, 2017
Short summary

Related subject area

Hydrological Hazards
Improving pluvial flood simulations with a multi-source digital elevation model super-resolution method
Yue Zhu, Paolo Burlando, Puay Yok Tan, Christian Geiß, and Simone Fatichi
Nat. Hazards Earth Syst. Sci., 25, 2271–2286, https://doi.org/10.5194/nhess-25-2271-2025,https://doi.org/10.5194/nhess-25-2271-2025, 2025
Short summary
It could have been much worse: spatial counterfactuals of the July 2021 flood in the Ahr Valley, Germany
Sergiy Vorogushyn, Li Han, Heiko Apel, Viet Dung Nguyen, Björn Guse, Xiaoxiang Guan, Oldrich Rakovec, Husain Najafi, Luis Samaniego, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 25, 2007–2029, https://doi.org/10.5194/nhess-25-2007-2025,https://doi.org/10.5194/nhess-25-2007-2025, 2025
Short summary
Rapid high-resolution impact-based flood early warning is possible with RIM2D: a showcase for the 2023 pluvial flood in Braunschweig
Shahin Khosh Bin Ghomash, Heiko Apel, Kai Schröter, and Max Steinhausen
Nat. Hazards Earth Syst. Sci., 25, 1737–1749, https://doi.org/10.5194/nhess-25-1737-2025,https://doi.org/10.5194/nhess-25-1737-2025, 2025
Short summary
The 2018–2023 drought in Berlin: impacts and analysis of the perspective of water resources management
Ina Pohle, Sarah Zeilfelder, Johannes Birner, and Benjamin Creutzfeldt
Nat. Hazards Earth Syst. Sci., 25, 1293–1313, https://doi.org/10.5194/nhess-25-1293-2025,https://doi.org/10.5194/nhess-25-1293-2025, 2025
Short summary
Recent large-inland-lake outbursts on the Tibetan Plateau: processes, causes, and mechanisms
Fenglin Xu, Yong Liu, Guoqing Zhang, Ping Zhao, R. Iestyn Woolway, Yani Zhu, Jianting Ju, Tao Zhou, Xue Wang, and Wenfeng Chen
Nat. Hazards Earth Syst. Sci., 25, 1187–1206, https://doi.org/10.5194/nhess-25-1187-2025,https://doi.org/10.5194/nhess-25-1187-2025, 2025
Short summary

Cited articles

Aguilera, P. A., Fernández, A., Fernández, R., Rumí, R., and Salmerón, A.: Bayesian networks in environmental modelling, Environ. Modell. Softw., 26, 1376–1388, https://doi.org/10.1016/j.envsoft.2011.06.004, 2011.
Bayraktarli, Y. Y. and Faber, M. H.: Bayesian probabilistic network approach for managing earthquake risks of cities, Georisk, 5, 2–24, https://doi.org/10.1080/17499511003679907, 2011.
Berkes, F.: Understanding uncertainty and reducing vulnerability: lessons from resilience thinking, Nat. Hazards, 41, 283–295, https://doi.org/10.1007/s11069-006-9036-7, 2007.
Blaser, L., Ohrnberger, M., Riggelsen, C., and Scherbaum, F.: Bayesian Belief Network for Tsunami Warning Decision Support, Lect. Notes. Artif. Int., 5590, 757–768, https://doi.org/10.1007/978-3-642-02906-6_65, 2009.
Blaser, L., Ohrnberger, M., Riggelsen, C., Babeyko, A., and Scherbaum, F.: Bayesian networks for tsunami early warning, Geophys. J. Int., 185, 1431–1443, 2011.
Download
Share
Altmetrics
Final-revised paper
Preprint