TY - JOUR
T1 - Probabilistic characterization of the vegetated hydrodynamic system using non-parametric bayesian networks
AU - Niazi, Muhammad Hassan Khan
AU - Morales Napoles, Oswaldo
AU - van Wesenbeeck, Bregje K.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Dependence modeling
KW - Mangroves
KW - Nature-based solutions
KW - Nonparametric bayesian networks
KW - Salt marshes
KW - Seagrasses
UR - http://www.scopus.com/inward/record.url?scp=85101203815&partnerID=8YFLogxK
U2 - 10.3390/w13040398
DO - 10.3390/w13040398
M3 - Article
AN - SCOPUS:85101203815
SN - 2073-4441
VL - 13
SP - 1
EP - 25
JO - Water (Switzerland)
JF - Water (Switzerland)
IS - 4
M1 - 398
ER -