Machine LearningBased Wave Model With High Spatial Resolution in Chesapeake Bay

AbstractA highresolution wave model is crucial for accurate modeling of sediment and organic material transports, but its computational costs hinder direct coupling to an ecosystem model. We developed a machine learning model using long shortterm memory to simulate largescale, highresolution waves. Trained with numerical wave model (NWM) outputs and wind data from nine locations, our model successfully replicates NWM results for daily mean significant wave height and period in Chesapeake Bay with identical spatial resolution. Compared to the NWM, the datadriven model has rootmeansquare errors below 6cm for daily mean significant wave height and 1s for the wave period in the bay. It demonstrates excellent model skills and can accurately forecast daily mean significant wave height and period at NOAA wave stations comparable to NWMs. Using minimal wind data and having a short runtime, our datadriven model shows promise as an alternative for wave forecasting and coupling with sediment and ecological models.

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