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Open AccessArticle

Probabilistic Characterization of the Vegetated Hydrodynamic System Using Non-Parametric Bayesian Networks

1
Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 CN Delft, The Netherlands
2
Marine and Coastal Systems, Deltares, 2629 HV Delft, The Netherlands
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Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in 37th International Conference on Coastal Engineering (2020).
Academic Editors: Marcel J. F. Stive and Fangxin Fang
Water 2021, 13(4), 398; https://doi.org/10.3390/w13040398
Received: 4 November 2020 / Revised: 24 January 2021 / Accepted: 26 January 2021 / Published: 4 February 2021
(This article belongs to the Special Issue Nature-Based Solutions for Coastal Engineering and Management)
The increasing risk of flooding requires obtaining generalized knowledge for the implementation of distinct and innovative intervention strategies, such as nature-based solutions. Inclusion of ecosystems in flood risk management has proven to be an adaptive strategy that achieves multiple benefits. However, obtaining generalizable quantitative information to increase the reliability of such interventions through experiments or numerical models can be expensive, laborious, or computationally demanding. This paper presents a probabilistic model that represents interconnected elements of vegetated hydrodynamic systems using a nonparametric Bayesian network (NPBN) for seagrasses, salt marshes, and mangroves. NPBNs allow for a system-level probabilistic description of vegetated hydrodynamic systems, generate physically realistic varied boundary conditions for physical or numerical modeling, provide missing information in data-scarce environments, and reduce the amount of numerical simulations required to obtain generalized results—all of which are critically useful to pave the way for successful implementation of nature-based solutions. View Full-Text
Keywords: nature-based solutions; seagrasses; salt marshes; mangroves; dependence modeling; nonparametric Bayesian networks nature-based solutions; seagrasses; salt marshes; mangroves; dependence modeling; nonparametric Bayesian networks
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MDPI and ACS Style

Niazi, M.H.K.; Morales Nápoles, O.; van Wesenbeeck, B.K. Probabilistic Characterization of the Vegetated Hydrodynamic System Using Non-Parametric Bayesian Networks. Water 2021, 13, 398. https://doi.org/10.3390/w13040398

AMA Style

Niazi MHK, Morales Nápoles O, van Wesenbeeck BK. Probabilistic Characterization of the Vegetated Hydrodynamic System Using Non-Parametric Bayesian Networks. Water. 2021; 13(4):398. https://doi.org/10.3390/w13040398

Chicago/Turabian Style

Niazi, Muhammad H.K.; Morales Nápoles, Oswaldo; van Wesenbeeck, Bregje K. 2021. "Probabilistic Characterization of the Vegetated Hydrodynamic System Using Non-Parametric Bayesian Networks" Water 13, no. 4: 398. https://doi.org/10.3390/w13040398

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