A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors
A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors
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DOI：
10.1016/j.ress.2023.109779
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Rajesh Kandel;H. Baroud
中科院分区：
文献类型：
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作者：
Rajesh Kandel;H. Baroud
北极气温上升导致海冰融化，使该地区更容易通过一条新的、更短的航线航行。由于极端的环境条件，在北极航行存在很高的风险。这项研究提出了可扩展的数据驱动预测模型，以评估不确定天气和海冰条件下北极航行的风险。机器学习用于预测北极事件的类型并确定其风险因素。研究了多个模型，并根据多个验证指标确定了西北航道（NWP）和北海航线（NSR）这两条北极航线的最准确模型。随机森林和朴素贝叶斯模型分别为 NWP 和 NSR 提供最佳精度和 F1 分数。风速、船只类型、长度和船龄是 NWP 的重要风险因素，而地表以上 2 m 的温度、船只长度和船龄是 NSR 的重要风险因素。部分依赖图用于研究每个特征对预测每种事件类型的影响。设备故障在较新和较长的船舶中更为常见。对于较长的船舶，更有可能预测到与碰撞相关的事件，而在较高的气温下，与搁浅相关的事件更频繁。
The rise in Arctic temperatures has caused sea ice to melt, making the region more navigable through a new, shorter shipping route. Navigating the Arctic poses high risks due to extreme environmental conditions. This study proposes scalable data-driven predictive models to assess the risk of Arctic navigation under uncertain weather and sea ice conditions. Machine learning is applied to predict the type of Arctic incidents and identify their risk factors. Several models are investigated, and the most accurate model for the two Arctic routes, the North West Passage (NWP) and the Northern Sea Route (NSR), is determined based on several validation metrics. Random forest and Naïve Bayes models provide the best accuracy and F1-score for NWP and NSR, respectively. The wind speed, vessel type, length, and age are important risk factors for NWP while temperature at 2 m above the surface, vessel length, and age are important for NSR. Partial dependence plots are used to investigate the effect of each feature on predicting each incident type. Equipment failures are more common among newer and longer vessels. Collision related incidents are more likely to be predicted for longer vessels, while grounding related incidents are more frequent at higher air temperature.