Keywords
Extreme conditions
probabilistic approach
Bayesian Network model
How to Cite
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.References
Charniak, E. (1991). Bayesian networks without tears: making bayesian networks more accessible to the probabilistically unsophisticated. AI Mag., 12(4):50-63.
Cooper, G. F. (1990). The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks. Artiï¬cial Intelligence, 42(2-3):393-405.http://dx.doi.org/10.1016/0004-3702(90)90060-D
Den Heijer, C., Baart, F., and van Koningsveld, M. (2011). Assessment of dune failure along the Dutch coast using a fully probabilistic approach. Geomorphology, 143-144:95-103.
Fishman, G. S. (1996). Monte Carlo: concepts, algorithms, and applications. Springer, New York.http://dx.doi.org/10.1007/978-1-4757-2553-7
Plant, N. G. and Stockdon, H. F. (2012). Probabilistic prediction of barrier-island response to hurricanes. Journal of Geophysical Research.http://dx.doi.org/10.1029/2011JF002326