C. (Kees) 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


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.


Dune erosion; Extreme conditions; probabilistic approach; Bayesian Network model


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