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The Application of Bayesian Networks in Natural Hazard Analyses : Volume 1, Issue 5 (22/10/2013)

By Vogel, K.

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Book Id: WPLBN0004019032
Format Type: PDF Article :
File Size: Pages 50
Reproduction Date: 2015

Title: The Application of Bayesian Networks in Natural Hazard Analyses : Volume 1, Issue 5 (22/10/2013)  
Author: Vogel, K.
Volume: Vol. 1, Issue 5
Language: English
Subject: Science, Natural, Hazards
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2013
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Scherbaum, F., Riggelsen, C., Korup, O., & Vogel, K. (2013). The Application of Bayesian Networks in Natural Hazard Analyses : Volume 1, Issue 5 (22/10/2013). Retrieved from http://www.worldebookfair.com/


Description
Description: Institute of Earth and Environmental Sciences, University of Potsdam, Germany. In natural hazards we face several uncertainties due to our lack of knowledge and/or the intrinsic randomness of the underlying natural processes. Nevertheless, deterministic analysis approaches are still widely used in natural hazard assessments, with the pitfall of underestimating the hazard with potentially disastrous consequences.

In this paper we show that the Bayesian network approach offers a flexible framework for capturing and expressing a broad range of different uncertainties as those encountered in natural hazard assessments. Although well studied in theory, the application of Bayesian networks on real-world data is often not straightforward and requires specific tailoring and adaption of existing algorithms. We demonstrate by way of three case studies (a ground motion model for a seismic hazard analysis, a flood damage assessment, and a landslide susceptibility study) the applicability of Bayesian networks across different domains showcasing various properties and benefits of the Bayesian network framework.

We offer suggestions as how to tackle practical problems arising along the way, mainly concentrating on the handling of continuous variables, missing observations, and the interaction of both. We stress that our networks are completely data-driven, although prior domain knowledge can be included if desired.


Summary
The application of Bayesian networks in natural hazard analyses

Excerpt
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