Sea State Parameter Prediction Based on Residual Cross-Attention

Abstract

The combination of onboard estimation and data-driven methods is widely applied for sea state parameter prediction. However, conventional data-driven approaches often exhibit limited adaptability to this task, resulting in suboptimal prediction performance. To enhance prediction accuracy, this study introduces Cross-Attention mechanisms to optimize the task of real-time sea state parameters prediction for maritime operations, innovatively develops a Residual Cross-Attention mechanism, and integrates it into representative networks for sea state parameter prediction. Three benchmark networks were selected, each evaluated under three configurations, without attention, with Cross-Attention, and with Residual Cross-Attention, resulting in a total of nine experimental scenarios for error assessment. The results demonstrate that both Cross-Attention and Residual Cross-Attention reduce prediction error to varying degrees and improve model robustness.