A Novel Data-Driven Prediction Framework for Ship Navigation Accidents in the Arctic Region

• Published in: Journal of Marine Science and Engineering
• Main Authors: Xue Yang, Jingkai Zhi, Wenjun Zhang, Sheng Xu, Xiangkun Meng
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: Arctic waters; arctic navigation safety; navigation accident; data driven; accident risk; Naval architecture. Shipbuilding. Marine engineering; VM1-989; Oceanography; GC1-1581; Arctic; Arctic Council
• Online Access: https://doi.org/10.3390/jmse11122300; https://doaj.org/article/12742fe2fb6c45fabf678692f7356892

Arctic navigation faces numerous challenges, including uncertain ice conditions, rapid weather changes, limited communication capabilities, and lack of search and rescue infrastructure, all of which increase the risks involved. According to an Arctic Council statistical report, a remarkable 2638 maritime accidents were recorded in Arctic waters between 2005 and 2017, showing a fluctuating upward trend. This study collected and analyzed ship accident data in Arctic waters to identify the various accident scenarios and primary risk factors that impact Arctic navigation safety. By utilizing data-driven algorithms, a model for predicting ship navigation accidents in Arctic waters was constructed, providing an in-depth understanding of the risk factors that make accidents more likely. The research findings are of practical significance for enhancing quantitative risk assessment, specifically focusing on the navigational risks in Arctic waters. The results of this study can assist maritime authorities and shipping companies in conducting risk analysis and implementing accident prevention measures for safe navigation in Arctic waters.