Combining process-based and data-driven approaches to forecast beach and dune change. (July 2022) - Itzkin, Michael; Moore, Laura J.; Ruggiero, Peter; Hovenga, Paige A.; Hacker, Sally D.
Abstract: Producing accurate hindcasts and forecasts with coupled models is challenging due to complex parameterizations that are difficult to ground in observational data. We present a calibration workflow that utilizes a series of machine learning algorithms paired with Windsurf, a coupled beach-dune model (Aeolis, the Coastal Dune Model, and XBeach), to produce hindcasts and forecasts of morphologic change along Bogue Banks, North Carolina. Neural networks paired with genetic algorithms allow us to fine tune calibration parameters for the hindcast, and then a long short-term memory neural network, trained on the hindcast, produces a 4-year forecast. We compare our hindcasts to observations from 2016 to 2017 and find they successfully reproduce observed modes of dune and beach change except for seaward growth of the dune face. We compare our forecasts to observations from 2016 to 2020 and find that they produce reasonably accurate predictions of dune change except when there are significant instances of erosion during the forecast period. Highlights: A neural network paired with a genetic algorithm was used to calibrate a one-year hindcast with a coupled numerical model. A Long Short-Term Memory (LSTM) neural network was trained on the hindcast to produce 4-year forecasts of dune morphology. Windsurf accurately reproduces observed modes of dune and beach change except for seaward growth of the dune.