Journal Article

Machine Learning Based Moored Ship Movement Prediction

Peña, Enrique, Figuero, Andrés, Sande, José, Rabuñal, Juan, Carro, Humberto, Guerra, Andrés, Alvarellos, Alberto, Costas, Raquel

2021

Overview

Several port authorities are involved in the R+D+i projects for developing port management decision-making tools. We recorded the movements of 46 ships in the Outer Port of Punta Langosteira (A Coruña, Spain) from 2015 until 2020. Using this data, we created neural networks and gradient boosting models that predict the six degrees of freedom of a moored vessel from ocean-meteorological data and ship characteristics. The best models achieve, for the surge, sway, heave, roll, pitch and yaw movements, a 0.99, 0.99, 0.95, 0.99, 0.98 and 0.98 R2 in training and have a 0.10 m, 0.11 m, 0.09 m, 0.9°, 0.11° and 0.15° RMSE in testing, all below 10% of the corresponding movement range. Using these models with forecast data for the weather conditions and sea state and the ship characteristics and berthing location, we can predict the ship movements several days in advance. These results are good enough to reliably compare the models’ predictions with the limiting motion criteria for safe working conditions of ship (un) loading operations, helping us decide the best location for operation and when to stop operations more precisely, thus minimizing the economic impact of cargo ships unable to operate.

• Publisher: MDPI AG
• Subject: Atmospheric models / Berthing / Cargo ships / Decision making / decision trees / deep learning / Economic impact / Economics / GDP / gradient boosting / Gross Domestic Product / Impact analysis / Infrastructure / Learning algorithms / Loading operations / Machine learning / Merchant ships / Meteorological data / Neural networks / Ocean models / Pitch (inclination) / Port authorities / Ports / Project management / Rolling motion / Sea state / Sea states / ship movement prediction / Ships / Training / Weather forecasting / Working conditions / Yaw / Yawing