IMPACT ASSESSMENT OF EXTREME STORM EVENTS USING A BAYESIAN NETWORK
No. 33 (2012), Coastal Management, Environment, and Risk
No. 33 (2012)

IMPACT ASSESSMENT OF EXTREME STORM EVENTS USING A BAYESIAN NETWORK

Coastal Management, Environment, and Risk
https://doi.org/10.9753/icce.v33.management.4
Published 2012-10-25
C. (Kees) den Heijer
Delft University of Technology
Dirk T.J.A. Knipping
Van Oord
Nathaniel G. Plant
U.S. Geological Survey
Jaap S.M. van Thiel de Vries
Deltares
Fedor Baart
Deltares
Pieter H.A.J.M. van Gelder
Delft University of Technology
ICCE 2012 Cover Image
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Keywords

Dune erosion
Extreme conditions
probabilistic approach
Bayesian Network model

How to Cite

Heijer, C. (Kees) den, Knipping, D. T., Plant, N. G., Thiel de Vries, J. S. van, Baart, F., & Gelder, P. H. van. (2012). IMPACT ASSESSMENT OF EXTREME STORM EVENTS USING A BAYESIAN NETWORK. Coastal Engineering Proceedings, 1(33), management.4. https://doi.org/10.9753/icce.v33.management.4
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Abstract

This paper describes an investigation on the usefulness of Bayesian Networks in the safety assessment of dune coasts. A network has been created that predicts the erosion volume based on hydraulic boundary conditions and a number of cross-shore profile indicators. Field measurement data along a large part of the Dutch coast has been used to train the network. Corresponding storm impact on the dunes was calculated with an empirical dune erosion model named duros+. Comparison between the Bayesian Network predictions and the original duros+ results, here considered as observations, results in a skill up to 0.88, provided that the training data covers the range of predictions. Hence, the predictions from a deterministic model (duros+) can be captured in a probabilistic model (Bayesian Network) such that both the process knowledge and uncertainties can be included in impact and vulnerability assessments.
https://doi.org/10.9753/icce.v33.management.4
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References

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