Data-driven models for vessel motion prediction and the benefits of physics-based information

• Published in: Applied ocean research Vol. 120; p. 102916
• Main Authors: Schirmann, Matthew L., Collette, Matthew D., Gose, James W.
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.03.2022; Elsevier BV
• Subjects: Data-driven models; Digital twin; Heaving; Machine learning; Motion forecasting; Movement; Neural networks; Physics; Physics-based models; Prediction models; Research vessels; Motion forecasting; Data-driven models; Physics-based models; Digital twin; Neural networks; Machine learning
• ISSN: 0141-1187; 1879-1549
• DOI: 10.1016/j.apor.2021.102916

Machine learning approaches, onboard measurements, and widely available wave forecast and hindcast data present an opportunity to develop predictive models for vessel motion forecasting. Detailed vessel motion forecasts would support underway and deployment decisions for safer and more efficient vessel operation. To demonstrate this application, ridge regression and neural network models for heave, pitch, and roll prediction were trained and tested using time-and-place specific, multidirectional wave model parameters as input. Additionally, the performance benefits of providing these predictive models with computationally efficient, physics-based model predictions (PBMPs) of heave, pitch, and roll as additional inputs were examined. Data from approximately 13,500 30-minute windows, measured aboard an operational research vessel, were used to train and test the data-driven models. Data from over 2,500 additional 30-minute windows, measured aboard a sister vessel, were also used to test the versatility of the trained models. The results of this study showed effective reduction of motion amplitude mean-squared error (MSE) values on multiple test datasets relative to the PBMPs alone. The results also showed that inclusion of PBMPs as input to the data-driven models was typically beneficial in terms of MSE reduction, stressing the importance of retaining physics-based information in data-driven models.