[3R4-OS-27] OS-27

[3R4-OS-27-03] Performance evaluation of machine learning-based sea state estimation using ship motion

〇Kazuma Iwase¹, Ulrik Dam Nielsen², Raphaël Emile Gilbert Mounet², Gaute Storhaug³ (1. Tokyo University of Marine Science and Technology, 2. Technical University of Denmark, 3. DNV)

[[Online]]

Keywords:Machine Learning, Sea State Estimation

This study aims to evaluate the performance of three machine learning-based methods for sea state estimation using ship motion data. The proposed methods treat ship motion as an analog to wave buoys, leveraging the measured responses to estimate sea states. All three methods rely on machine learning but provide different outputs: Method 1 outputs wave parameters, Method 2 provides point wave spectrum data and wave direction, and Method 3 generates the directional wave spectrum in a non-parametric form. While these methods demonstrate good performance using data derived from physical simulations, this study also employs observation data from wave radar collected aboard an in-service container ship for training and testing. The results show that all three methods perform well on the observation data.