Shallow and deep learning models for vessel motions forecasting during adverse weather conditions

Accurately forecasting vessel motions is a critical step towards achieving fast and accurate intelligent vessel control systems. Intelligent vessel control relies on accurate predictions of vessel motion to make informed decisions regarding control, maneuvering, and positioning, particularly during times of exogenous loading caused by adverse weather conditions. Hence, by accurately forecasting vessel motion accurately, the control system can anticipate potential issues (i.e., excessive trim or roll) and prescribe corrective actions before they become problematic. In this study, the authors propose two approaches to address the problem of vessel motion forecasting. The first approach relies on classical shallow learning models, whereas the second approach involves the use of state-of-the-art deep learning models for improved accuracy at further forecast horizons.