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.

中文翻译：

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基于机器学习的系泊船舶运动预测

多个港口当局参与了开发港口管理决策工具的 R+D+i 项目。从 2015 年到 2020 年，我们记录了 46 艘船在 Punta Langosteira 外港（西班牙拉科鲁尼亚）的运动。使用这些数据，我们创建了神经网络和梯度提升模型，以预测停泊船只从海洋中的六个自由度- 气象数据和船舶特性。对于喘振、摇摆、升沉、滚转、俯仰和偏航运动，最好的模型达到 0.99、0.99、0.95、0.99、0.98 和 0.98 R ²在训练中，并且在测试中具有 0.10 m、0.11 m、0.09 m、0.9°、0.11° 和 0.15° RMSE，均低于相应运动范围的 10%。使用这些模型以及天气条件和海况以及船舶特性和停泊位置的预测数据，我们可以提前几天预测船舶的运动。这些结果足以可靠地将模型的预测与船舶（未）装载操作的安全工作条件的限制运动标准进行比较，帮助我们更准确地确定最佳操作位置以及何时停止操作，从而最大限度地减少经济影响无法操作的货船。