Real-Time Mooring Tension Prediction for Semi-Submersible Aquaculture Platforms by an EEMD-TCN-SA Neural Network Approach

Abstract

Precise prediction of mooring tension is essential for the safety and operational efficiency of semi-submersible aquaculture platforms. Traditional numerical methods struggle with real-time performance due to the nonlinear and dynamic characteristics of environmental loads. This study proposes a novel neural network approach to enhance real-time forecasting of mooring line responses, combining Ensemble Empirical Mode Decomposition (EEMD), Temporal Convolutional Networks (TCNs), and a Self-Attention (SA) mechanism. The training dataset encompasses time-domain analysis results, including mooring tensions, motion responses, and total structural forces. Firstly, Pearson Correlation Analysis (PCA) is utilized to assess the linear relationships among the hydrodynamic variables. Subsequently, EEMD is applied to decompose the mooring tension data, which is then combined with the highly correlated variables to form the input dataset. Finally, the TCN model is trained to predict the time series, while an SA mechanism is integrated to weigh the significance of different moments within the sequence, thereby further enhancing prediction accuracy. The results demonstrate that the evaluation metrics of the EEMD-TCN-SA model outperform those of other neural network models, effectively predicting mooring tension for semi-submersible platforms and significantly reducing prediction errors.

Bibliographic Information