A data mining method for automatic identification and analysis of icebreaker assistance operation in ice-covered waters

• Published in: Ocean Engineering
• Main Authors: Liu, Cong, Musharraf, Mashrura, Li, Fang, Kujala, Pentti
• Other Authors: Department of Mechanical Engineering, Marine Technology, Aalto-yliopisto, Aalto University
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier Ltd 2022
• Subjects: Convoy operations; Data mining; Escort operations; Ice-covered waters; Maritime safety; Ship performance in ice; Icebreaker; Sea ice; ice covered waters
• Online Access: https://aaltodoc.aalto.fi/handle/123456789/117621; https://doi.org/10.1016/j.oceaneng.2022.112914

Funding Information: This work was supported by the Academy of Finland, Finland: Towards human centered intelligent ships for winter navigation (Decision number: 351491), and the AI-based simulation grant for intelligent ice navigation (Grant number: W22-1 SIMNAV) funded by Winter Navigation Research Board. The authors also thank Ketki Kulkarni from Aalto University, Jarkko Toivola and Tuomas Taivi from FTIA, and FMI for providing practical knowledge and data. Funding Information: This work was supported by the Academy of Finland, Finland : Towards human centered intelligent ships for winter navigation (Decision number: 351491 ), and the AI-based simulation grant for intelligent ice navigation (Grant number: W22-1 SIMNAV) funded by Winter Navigation Research Board. The authors also thank Ketki Kulkarni from Aalto University, Jarkko Toivola and Tuomas Taivi from FTIA, and FMI for providing practical knowledge and data. Publisher Copyright: © 2022 The Authors Icebreaker assistance is a common but complex operation in ice-infested regions. Currently, the operational decision-making and the decisions regarding the safety indicators are primarily based on expert knowledge, resulting in subjectivity and the ad hoc nature of icebreaker assistance. This can negatively impact both the navigational efficiency of icebreaker services and the productivity of port services. This paper proposes a data mining method to automatically identify icebreaker assistance cases from big data. The identified cases are then used to statistically analyze the safety indicators. The data used in the paper include navigational data obtained from the Automatic Identification System (AIS) and sea ice data in the Baltic Sea area. A multi-step clustering method is adopted to cluster similar trajectories of merchant vessels and icebreakers, identifying assistance events automatically. The results show that the proposed method can automatically identify icebreaker assistance cases with an accuracy of 99.6%, precision of 87%, and recall of 78.3%. ...