World Library  

Add to Book Shelf
Flag as Inappropriate
Email this Book

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

By Vogel, K.

Click here to view

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
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

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

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.

The application of Bayesian networks in natural hazard analyses

Blaser, L., Ohrnberger, M., Riggelsen, C., and Scherbaum, F.: Bayesian Belief Network for Tsunami Warning Decision Support, Lect. Notes. Artif. Int., 5590, 757–768, doi: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.; Boore, D.: Simulation of ground motion using the stochastic method, Pure Appl. Geophys., 160, 635–676, 2003.; Castelo, R. and Kocka, T.: On inclusion-driven learning of Bayesian networks, J. Mach. Learn. Res., 4, 527–574, 2003.; Chickering, D. M.: Optimal structure identification with greedy search, J. Mach. Learn. Res., 3, 507–554, 2002.; Elmer, F., Thieken, A. H., Pech, I., and Kreibich, H.: Influence of flood frequency on residential building losses, Nat. Hazards Earth Syst. Sci., 10, 2145–2159, doi:10.5194/nhess-10-2145-2010, 2010.; Friedman, N.: Learning belief networks in the presence of missing values and hidden variables, Fourteenth International Conference on Machine Learning, July 1997, Nashville, TN, 125–133, 1997.; Friedman, N.: The Bayesian structural EM algorithm, Fourteenth conference on Uncertainty in artificial intelligence, 24–26 July 1998, Madison, WI, 129–138, 1998.; Friedman, N. and Koller, D.: Being Bayesian about network structure, Sixteenth conference on Uncertainty in artificial intelligence, 30 June–3 July 2000, Stanford, CA, 201–210, 2000.; Friedman, N., Goldszmidt, M., and Wyner, A.: Data analysis with Bayesian networks: a bootstrap approach, Fifteenth conference on Uncertainty in artificial intelligence, 30 July–1 August 1999, Stockholm, Sweden, 196–205, 1999.; Koller, D. and Friedman, N.: Probabilistic Graphical Models: Principles and Techniques, The MIT Press, 2009.; Korup, O. and Stolle, A.: Landslide Prediction from Machine Learning, Geology Today, submitted, 2013.; Kuehn, N. M., Riggelsen, C., and Scherbaum, F.: Modeling the joint probability of earthquake, site, and ground-motion parameters using Bayesian networks, B. Seismol. Soc. Am., 101, 235–249, 2011.; Langseth, H. and Nielsen, T. D.: Parameter estimation in mixtures of truncated exponentials, 4th European Workshop on Probabilistic Graphical Models, 17–19 September 2008, Hirtshals, Denmark, 169–176, 2008.; Langseth, H., Nielsen, T. D., Rumí, R., and Salmerón, A.: Inference in hybrid Bayesian networks, Reliab. Eng. Syst. Safe., 94, 1499–1509, 2009.; Langseth, H., Nielsen, T. D., Rumí, R., and Salmerón, A.: Parameter estimation and model selection for mixtures of truncated exponentials, Int. J. Approx. Reason., 51, 485–498, 2010.; Little, R. and Rubin, D.: Statistical Analysis with Missing Data, vol. 4, Wiley, New York, 1987.; Merz, B., Kreibich, H., Schwarze, R., and Thieken, A.: Review article Assessment of economic flood damage, Nat. Hazards Earth Syst. Sci., 10, 1697–1724, doi:10.5194/nhess-10-1697-2010, 2010.; Riggelsen, C.: MCMC learning of Bayesian network models by Markov blanket decomposition, in: Machine Learning: ECML 2005, Springer, Berlin, Heidelberg, 329–340, 2005.; Monti, S., and Cooper, G. F.: A multivariate discretization method for learning Bayesian networks from mixed data, Fourteenth conference on Uncertainty in artificial intelligence, 24–26 July 1998, Madison, WI, 404–413, 1998.; Moral, S., Rumí, R., and Salmerón, A.: Mixtures of truncated exponentials in hybrid Bayesian networks, in: Symbolic and Quantitative Approaches to Reasoning with Uncertainty, edited by: Benferhat, S. and Besnard, P., Springer, Berlin, Heidelberg, 156–167, 2001.; Riggelsen, C.: Learning Bayesian networks from incomplete data: an efficient method for generating approximate predictive distributions, SIAM International conf. on data mining, 20–22 April 2006, Bethesda, Maryland, 130


Click To View

Additional Books

  • Monitoring Subsidence Effects in the Urb... (by )
  • Are Floods in Part a Form of Land Use Ex... (by )
  • Brief Communication: Catalyst – a Multi-... (by )
  • Rapid Characterisation of Large Earthqua... (by )
  • Evaluation of a Compound Distribution Ba... (by )
  • The Challenge of Installing a Tsunami Ea... (by )
  • Avalanche Situation in Turkey and Back C... (by )
  • Analysis of Earthquake Parameters to Gen... (by )
  • Orientation Effects of Horizontal Seismi... (by )
  • Improving the Calibration of the Best Me... (by )
  • Laboratory Tests for the Optimization of... (by )
  • Integrated Approach for Coastal Hazards ... (by )
Scroll Left
Scroll Right


Copyright © World Library Foundation. All rights reserved. eBooks from World eBook Fair are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.