Geology

Meteorology

Nowcasting

Machine learning

Recurrent neural network

Time series

Engineering

Series (stratigraphy)

Paleontology

Artificial intelligence

Marine engineering

Geography

Set (abstract data type)

Programming language

Long-term prediction

Computer science

Data set

Artificial neural network

Telecommunications

Stern

Journal of Ocean Engineering and Marine Energy

The prediction capability of recurrent-type neural networks is investigated for real-time short-term prediction (nowcasting) of ship motions in high sea state. Specifically, the performance of recurrent neural networks, long short-term memory, and gated recurrent units models are assessed and compared using a data set coming from computational fluid dynamics simulations of a self-propelled destroyer-type vessel in stern-quartering sea state 7. Time-series of incident wave, ship motions, rudder angle, as well as immersion probes, are used as variables for a nowcasting problem. The objective is to obtain about 20 s ahead prediction. Overall, the three methods provide promising and comparable results.

Time-series forecasting for ships maneuvering in waves via recurrent-type neural networks