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

Cong Liu*, Mashrura Musharraf, Fang Li, Pentti Kujala

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

16 Citations (Scopus)
61 Downloads (Pure)

Abstract

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%. The automatic identification along with the statistical analysis can assist in the development of an intelligent decision-making system for safe and efficient winter navigation.

Original languageEnglish
Article number112914
Number of pages16
JournalOcean Engineering
Volume266
DOIs
Publication statusPublished - 15 Dec 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • Convoy operations
  • Data mining
  • Escort operations
  • Ice-covered waters
  • Maritime safety
  • Ship performance in ice

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