  Ship roll motion in high sea states has large amplitudes and nonlinear
dynamics, and its prediction is significant for operability, safety, and
survivability. This paper presents a novel data-driven methodology to provide a
multi-step prediction of ship roll motions in high sea states. A hybrid neural
network is proposed that combines long short-term memory (LSTM) and
convolutional neural network (CNN) in parallel. The motivation is to extract
the nonlinear dynamic characteristics and the hydrodynamic memory information
through the advantage of CNN and LSTM, respectively. For the feature selection,
the time histories of motion states and wave heights are selected to involve
sufficient information. Taken a scaled KCS as the study object, the ship
motions in sea state 7 irregular long-crested waves are simulated and used for
the validation. The results show that at least one period of roll motion can be
accurately predicted. Compared with the single LSTM and CNN methods, the
proposed method has better performance in predicting the amplitude of roll
angles. Besides, the comparison results also demonstrate that selecting motion
states and wave heights as feature space improves the prediction accuracy,
verifying the effectiveness of the proposed method.
