Bayesian Networks and Barrier Island Morphodynamics
In past roles at University of Florida and USGS St. Petersburg Coastal and Marine Science Center, I worked on two projects that developed and implemented Bayesian statistical frameworks to investigate barrier island morphodynamics. The first (Wilson et al., 2015) was designed to hindcast the location and intensity of erosion on the beach and dune system at Fire Island, NY. The second (Wilson et al., 2019) predicted the location and magnitude of post-Hurricane Sandy barrier island recovery. Both projects were in collaboration with USGS and the National Park Service and provided information for management decisions and allocation of resources within Fire Island National Seashore.
Application of Bayesian Networks to hindcast barrier island morphodynamics
Wilson et al., 2015
Site-specific probabilistic models of coastal change are reliable because they are formulated with observations so that local factors, of potentially high influence, are inherent in the model. The development and use of predictive tools such as Bayesian Networks in response to future storms has the potential to better inform management decisions and hazard preparation in coastal communities. We present several Bayesian Networks designed to hindcast distinct morphologic changes attributable to the Nor'Ida storm of 2009, at Fire Island, New York. Model predictions are informed with historical system behavior, initial morphologic conditions, and a parameterized treatment of wave climate.
A Bayesian Approach to Predict Sub-Annual Beach Change and Recovery
Wilson et al., 2019
We developed and tested a Bayesian network to predict the cross-shore position of an upper beach elevation contour following 1 month to 1-year intervals at Fire Island, New York. We combine hydrodynamic data with series of island-wide topographic data and spatially limited cross-shore profiles. This experiment shows that data collection techniques with different spatial and temporal frequencies can be used to inform a single modeling framework and can provide insight to BN training requirements. Overall, results indicate that BNs and inputs can be developed for broad coastal change assessment or tailored to a set of predictive requirements, making this methodology applicable to a variety of coastal prediction scenarios.
