This study introduces an artificial neural network system for ship motion prediction in seaways. To consider the physical characteristics of wave-induced ship motions, neural networks based on a Long Short-Term Memory (LSTM) encoder and decoder, and a convolutional neural network (CNN) are integrated. The LSTM encoder computes the state vector representing the memory effects of motion-induced radiated waves based on past motion records, that is, a sequence-to-one model. In the LSTM decoder, the motion time series is predicted using the encoded initial state vector and foreseen information on the ocean wave field around a vessel, that is, a sequence-to-sequence model. In addition, a CNN is adopted to compress the wave data into a vector sequence. Particularly, the present CNN uses spatiotemporal wave-field data, not a wave signal at single location. To validate the proposed system, a database for training the integrated system was constructed using a physics-based seakeeping program for various sea states. By applying the trained model, deterministic predictions were performed for a new ocean environment, and the accuracy and reliability of the testing results are investigated according to the input data and neural network structures. From the simulation results, it was confirmed that the present encoder–decoder system can conduct ship motion forecasting by effectively considering the motion memory effects and wave excitations as in the ship hydrodynamic model. In addition, excitations and resulting motion responses by short-crested waves can be considered through CNN-based wave-field data processing. Finally, the present machine-learning model also showed the capability of extracting ship operation information (maneuvering quantities) from the given wave-field data.

Accurate prediction of ship motion is essential for ensuring the safety and efficiency of maritime operations. However, the ship dynamics’ nonlinear, non-stationary, and environment-dependent nature presents significant challenges for reliable short-term forecasting. This study uses a simulated dataset designed to reflect realistic maritime variability to evaluate the performance of recurrent neural network (RNN)-based models—including RNN, LSTM, GRU, and Bi-LSTM—under both single and multi-environment conditions. The analysis examines the effects of input sequence length, downsampling intervals, model complexity, and input dimensionality. Results show that Bi-LSTM consistently outperforms unidirectional architectures, particularly in complex multi-environment scenarios. In single-environment settings, the prediction horizon exceeded 40 s, while it decreased to around 20 s under more variable conditions, reflecting generalization challenges. Multi-degree-of-freedom (DOF) inputs enhanced performance by capturing the coupled nature of ship dynamics, whereas incorporating wave height data yielded inconsistent results. A sequence length of 200 timesteps and a downsampling interval of 5 effectively balanced motion feature preservation with high-frequency noise reduction. Increasing model size improved accuracy up to 256 hidden units and 10 layers, beyond which performance gains diminished. Additionally, Peak Matching was introduced as a complementary metric to MSE, emphasizing the importance of accurately predicting motion extrema for practical maritime applications.