Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.本文提出两款数据驱动的概率模型，可基于平整冰（level ice）厚度、密集度、冰脊（ice ridges）、堆集冰（rafted ice）等冰情特征的联合效应，并考虑冰体挤压的影响，实现船舶航速与船舶可能被困冰中场景的预测。 为构建上述模型，本研究采用了两类数据集：其一为来自自动识别系统（Automatic Identification System）的选定船舶运行性能数据；其二为芬兰气象研究所（Finnish Meteorological Institute）开发的数值冰模型HELMI所提供的冰情场数据。本研究通过贝叶斯学习算法（Bayesian learning algorithms）建立了冰情与船舶运动之间的关联关系。 本文所开展的案例研究，针对一艘冰级散货船（ice-strengthened bulk carrier）在时空变化的复杂冰情条件下，往返于芬兰两座港口的单次无护航航行展开。研究结果表明，所提模型具备优异的预测性能：在指定区间内对船舶航速的预测准确率平均可达80%，对船舶可能被困冰中场景的预测准确率则超过90%。 本研究期望该新方法能够助力冰覆盖水域的航线安全高效规划问题，其中船舶运行性能可作为目标函数的考量维度。