Real-time ship motion prediction based on adaptive wavelet transform and dynamic neural network

• Published in: Ocean engineering 2023-07, Vol.280, p.114466, Article 114466
• Main Authors: Gao, Nan, Hu, Ankang, Hou, Lixun, Chang, Xin
• Format: Article
• Language: English
• Subjects: Adaptive wavelet decomposition; Dynamic neural network; Real-time prediction; Residual recurrent neural network; Ship motion
• ISSN: 0029-8018; 1873-5258
• DOI: 10.1016/j.oceaneng.2023.114466

Ship motion attitude is the time series data with highly nonlinear and non-stationary characteristics significantly affected by environmental factors. To accurately predict the ship motion attitude in real-time, a ship motion attitude prediction model based on the Adaptive Discrete Wavelet Transform Algorithm (ADWT) and the space-time Residual Recurrent Neural Network (RRNN) with a time-varying structure is proposed. The ADWT decomposes the original data into components easier to predict, each element in the optimal component combination is predicted using the RRNN, the structure and parameters of which are adjusted in real-time with the sliding of data window. The model performance tests are conducted based on the simulation data of the ship motion of DTMB5415. The results show that the subsequences decomposed by the ADWT are easier to predict. Compared with other prediction models, the prediction accuracy of the ADWT-RRNN is the highest under all working conditions, its prediction accuracy and stability of it do not fluctuate significantly over a long prediction period. Hence, the more severe the sea states, the more pronounced the performance advantage is over other models. Finally, a reliable and efficient tool is eventually provided for real-time and accurate prediction of ship motion. •The adaptive discrete wavelet transform algorithm make the optimal decomposition level can be confirmed adaptively.•The space-time residual structure significantly improve the prediction accuracy compared to other models.•The time-varying structure make the prediction accuracy can be kept in an extremely high level over a long prediction period.•The advantage of the ADWT-RRNN will gradually increase in rougher sea condition.