Comparing machine learning-based sea state estimates by the wave buoy analogy.

This paper presents a comparison of three different machine learning frameworks applied in the wave buoy analogy used for estimating the sea state from measured ship responses. The three frameworks output and characterise the sea state in different ways: Model 1 outputs integral parameters, Model 2 outputs a point wave spectrum and the wave direction, Model 3 outputs the full directional wave spectrum. The assessment of the models is based on simulated motion measurements, i.e. synthetic data. In the particular investigations made, the performance of Model 2, relying on a novel framework, is generally superior. However, the central take-away from the study, is the importance of considering thorough and well-prepared training data encompassing many, not to say all, possible parameter combinations and shapes in the studied wave spectra forming the training data; any machine learning model is no better than the data upon which it is trained. • Machine learning applied in the wave buoy analogy. • Comparison of three fundamentally different ML models. • Presentation of a novel framework outputting a point wave spectrum and wave direction. • Thorough investigations of the models' generalisation capability. • Well-prepared and comprehensive training data is crucial. [ABSTRACT FROM AUTHOR]