Abstract
Vegetation as a nature-based solution for increasing flood risk has convincingly shown potential for flood hazard (wave load) reduction but lacks generalized results. In this study we have introduced stochastic dependence modeling using non-parametric Bayesian networks (NPBN) for vegetated coastal systems where the system was parametrized using continuous marginal distributions, and likely (conditional) correlations among variables. The model represented a consistent joint probability distribution and hence can be used to generate physically realistic conditions in data-scare environments. It adds value to numerical modeling by reducing the number of simulations required to get meaningful generalized results. Main findings, that were derived by using a NPBN, help to pave way for implementation of nature-based solutions for a range of realistic conditions that can be found across global coastal foreshores.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/T6TP0DH0qMw
Highlights
Coastal flood risk is an alarming threat due to increasing hazard and vulnerability owing to climate and anthropogenic changes
There is a need for generalized wave load reduction results
Mostly discrete Bayesian networks (BN) with conditional probability tables have been used which are mostly quantified with synthetic datasets requiring an impractical number of numerical simulations
Summary
Coastal flood risk is an alarming threat due to increasing hazard and vulnerability owing to climate and anthropogenic changes. Muhammad Hassan Khan Niazi, Delft University of Technology, M.H.K.Niazi@tudelft.nl, hassanniazi.ce@gmail.com Oswaldo Morales Nápoles, Delft University of Technology, O.MoralesNapoles@tudelft.nl Bregje K. van Wesenbeeck, Deltares, Bregje.vanWesenbeeck@deltares.nl Vegetation as a nature-based solution, along with conventional solutions like dikes, has convincingly shown potential for flood hazard (wave load) reduction.
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