A Semi-Supervised Machine Learning Model to Forecast Movements of Moored Vessels

Abstract

The good performance of the port activities in terminals is mainly conditioned by the dynamic response of the moored ship system at a berth. An adequate definition of the highly multivariate processes involved in the response of a moored ship at a berth is crucial for an appropriate characterization of port operability. The availability of an efficient forecast system of the movements of moored ships is essential for the planning, performance, and safety of the development of port operations. In this paper, an inference model to predict moored ship motions, based on a semi-supervised Machine Learning methodology, is presented. A comparison with different supervised and unsupervised Machine Learning techniques, as well as with existing Deep Learning-based models for predicting moored ship motions, has been performed. The highest performance of the semi-supervised Machine Learning-based model has been obtained. Additionally, the influence of infragravity wave parameters introduced as predictor variables in the model has been analyzed and compared with the typical ocean waves, wind, and sea level as predictor variables. The prediction model has been developed and validated with an available dataset of measured data from field campaigns in the Outer Port of Punta Langosteira (A Coruña, Spain).

A Decision-Making Tool for Port Operations Based on Downtime Risk and Met-Ocean Conditions including Infragravity Wave Forecast

“…These limits are used in the assessment of downtime with respect to the behavior of a particular vessel berthed in a specific port [10][11][12]. For this reason, the response of the ship-lines-fenders system is sometimes simulated with a numerical model and recently additionally through machine-learning tools [13,14].…”