Machine Learning Based Moored Ship Movement Prediction

Journal of Marine Science and Engineering

Juan Rabuñal¹  Andrés Guerra²  Alberto Alvarellos³  Humberto Carro⁴  Andrés Figuero⁴  Raquel Costas⁴  José Sande⁴  Enrique Peña⁴ 

[1] Center for Technological Innovation in Construction and Civil Engineering (CITEEC), RNASA Group, Computer Science Department, Universidade da Coruña, Campus Elviña s/n, 15071 A Coruña, Spain;Port Authority of A Coruña, Avenida de la Marina 3, 15001 A Coruña, Spain;Software Engineering Laboratory (ISLA), Computer Science Department, Universidade da Coruña, Campus Elviña s/n, 15071 A Coruña, Spain;Water and Environmental Engineering Group (GEAMA), Universidade da Coruña, Campus Elviña s/n, 15071 A Coruña, Spain;

关键词: machine learning;    neural networks;    deep learning;    gradient boosting;    decision trees;    ship movement prediction;   

DOI  :  10.3390/jmse9080800

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown