Impact assessment of extreme storm events using a Bayesian network

C. den Heijer; Dirk T.J.A. Knipping; Nathaniel G. Plant; Jaap S. M. van Thiel de Vries; Fedor Baart; Pieter H. A. J. M. van Gelder

doi:10.9753/icce.v33.management.4

USGS · 70118345

Impact assessment of extreme storm events using a Bayesian network

C. den Heijer·Dirk T.J.A. Knipping·Nathaniel G. Plant·Jaap S. M. van Thiel de Vries·Fedor Baart·Pieter H. A. J. M. van Gelder

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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.

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BibTeX RIS

C. den Heijer, Dirk T.J.A. Knipping, Nathaniel G. Plant, Jaap S. M. van Thiel de Vries, Fedor Baart, Pieter H. A. J. M. van Gelder. 2012-10-25. Impact assessment of extreme storm events using a Bayesian network. https://doi.org/10.9753/icce.v33.management.4

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