Estimation of sea state parameters from ship motion responses using attention-based neural networks

On-site estimation of sea state parameters is crucial for ship navigation. Extensive research has been conducted on model-based estimation utilizing ship motion responses. Model-free approaches based on machine learning (ML) have recently gained popularity, and estimation from time-series of ship motion responses using deep learning (DL) methods has given promising results. In this study, we apply the novel, attention-based neural network (AT-NN) for estimating wave height, zero-crossing period, and relative wave direction from raw time-series data of ship pitch, heave, and roll. Despite reduced input data, it has been demonstrated that the proposed approaches by modified state-of-the-art techniques (based on convolutional neural networks (CNN) for regression, multivariate long short-term memory CNN, and sliding puzzle neural network) improved estimation MSE, MAE, and NSE by up to 86%, 66%, and 56%, respectively, compared to the best performing original methods for all sea state parameters. Furthermore, the proposed technique based on AT-NN outperformed all tested methods (original and enhanced), improving estimation MSE by 94%, MAE by 74%, and NSE by 80% when considering all sea state parameters. Finally, we proposed a novel approach for interpreting the uncertainty estimation of neural network outputs based on the Monte-Carlo dropout method to enhance the model’s trustworthiness.