Estimation of sea state parameters from ship motion responses using attention-based neural networks
نویسندگان
چکیده
On-site estimation of sea state parameters is crucial for ship navigation. Extensive research has been conducted on model-based utilizing motion responses. Model-free approaches based machine learning (ML) have recently gained popularity, and from time-series responses using deep (DL) methods given promising results. In this study, we apply the novel, attention-based neural network (AT-NN) estimating wave height, zero-crossing period, relative direction raw data pitch, heave, roll. Despite reduced input data, it demonstrated that proposed by modified state-of-the-art techniques (based convolutional networks (CNN) regression, multivariate long short-term memory CNN, sliding puzzle network) improved MSE, MAE, NSE up to 86%, 66%, 56%, respectively, compared best performing original all parameters. Furthermore, technique AT-NN outperformed tested (original enhanced), improving MSE 94%, MAE 74%, 80% when considering Finally, a novel approach interpreting uncertainty outputs Monte-Carlo dropout method enhance model’s trustworthiness.
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ژورنال
عنوان ژورنال: Ocean Engineering
سال: 2023
ISSN: ['1873-5258', '0029-8018']
DOI: https://doi.org/10.1016/j.oceaneng.2023.114915