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The prediction capability of recurrent-type neural networks is investigatedfor real-time short-term prediction (nowcasting) of ship motions in high seastate. Specifically, the performance of recurrent neural networks, long-shortterm memory, and gated recurrent units models are assessed and compared using adata set coming from computational fluid dynamics simulations of aself-propelled destroyer-type vessel in stern-quartering sea state 7. Timeseries of incident wave, ship motions, rudder angle, as well as immersionprobes, are used as variables for a nowcasting problem. The objective is toobtain about 20 s ahead prediction. Overall, the three methods providepromising and comparable results.

Recurrent-type Neural Networks for Real-time Short-term Prediction of Ship Motions in High Sea State