Real-Time Prediction of Multi-Degree-of-Freedom Ship Motion and Resting Periods Using LSTM Networks

This study presents a novel real-time prediction technique for multi-degree-of-freedom ship motion and resting periods utilizing Long Short-Term Memory (LSTM) networks. The primary objective is to enhance the safety and efficiency of shipborne helicopter landings by accurately predicting heave, pitch, and roll data over an 8 s forecast horizon. The proposed method utilizes the LSTM network's capability to model complex nonlinear time series while employing the User Datagram Protocol (UDP) to ensure efficient data transmission. The model's performance was validated using real-world ship motion data collected across various sea states, achieving a maximum prediction error of less than 15%. The findings indicate that the LSTM-based model provides reliable predictions of ship resting periods, which are crucial for safe helicopter operations in adverse sea conditions. This method's capability to provide real-time predictions with minimal computational overhead highlights its potential for broader applications in marine engineering. Future research should explore integrating multi-model fusion techniques to enhance the model's adaptability to rapidly changing sea conditions and improve the prediction accuracy.