2025/05/31 by Morris-Thomas, Michael T. M. B., Martens, Marius
Engineering · Environmental Science · #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Hydrological Forecasting Using AI #Machine Learning (cs.LG) #Ship Hydrodynamics and Maneuverability #Statistics and Probability (physics.data-an) #Wave and Wind Energy Systems
paper · pdf · doi:10.48550/arxiv.2506.15713
openalex publication_date 2025/05/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The real-time prediction of floating offshore asset behavior under stochastic metocean conditions remains a significant challenge in offshore engineering. While traditional empirical and frequency-domain methods work well in benign conditions, they struggle with both extreme sea states and nonlinear responses. This study presents a supervised machine learning approach using multivariate regression to predict the nonlinear motion response of a turret-moored vessel in 400 m water depth. We developed a machine learning workflow combining a gradient-boosted ensemble method with a custom passive weathervaning solver, trained on approximately 106 samples spanning 100 features. The model achieved mean prediction errors of less than 5% for critical mooring parameters and vessel heading accuracy to within 2.5 degrees across diverse metocean conditions, significantly outperforming traditional frequency-domain methods. The framework has been successfully deployed on an operational facility, demonstrating its efficacy for real-time vessel monitoring and operational decision-making in offshore environments.