Journals / Marine Science and Technology Bulletin / 2025 / Volume: 14 Issue: 4

Identifying the association levels of marine accidents involving tankers in the Baltic Sea region

Coşkan Sevgili(Zonguldak Bülent Ecevit University, Maritime Faculty, Department of Marine Transportation Engineering, Kepez Campus, 67300, Zonguldak, Türkiye)0000-0003-3929-079X
Burak Kundakçı(Iskenderun Technical University, Barbaros Hayrettin Naval Architecture and Maritime Faculty, Department of Marine Transportation Engineering, Central Campus, 31200, Hatay, Türkiye)0000-0003-2294-0610
Pages
—
Publication Date
2025-12-31
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results of the analysis, ‘non-Flag of convenience (non-FOC)’, ‘small size tankers’, ‘vessels over 12 years old’, ‘chemical tankers’ and ‘loaded vessels’ were found to be the most frequent items in forming of the rules.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Maritime transportation is the backbone of global trade, and the Baltic Sea region is one of the busy waterways in the world.
📊
Key Findings & Data:Increase in marine traffic brings the possibility of marine accidents.
💡
Conclusion & Impact:According to the results of the analysis, ‘non-Flag of convenience (non-FOC)’, ‘small size tankers’, ‘vessels over 12 years old’, ‘chemical tankers’ and ‘loaded vessels’ were found to be the most frequent items in forming of the rules.
🔬Design:Research Article
📍Scope:2000 Verileri
⚙️Variables:Apriori algorithm · Association rule mining · Marine accidents

Abstract

Maritime transportation is the backbone of global trade, and the Baltic Sea region is one of the busy waterways in the world. Increase in marine traffic brings the possibility of marine accidents. There are significant risks for the human life, vessel and environment with these accidents. The aim of this research is to analyse the marine accidents in the Baltic Sea region and determine the relationship between the factors of these accidents with Association Rule Mining method. In this context, 278 marine accident reports between 2000 and 2023 for the Baltic Sea region are analysed with Apriori algorithm. According to the results of the analysis, ‘non-Flag of convenience (non-FOC)’, ‘small size tankers’, ‘vessels over 12 years old’, ‘chemical tankers’ and ‘loaded vessels’ were found to be the most frequent items in forming of the rules. When the rules according to the accident severity are analysed, ‘Canal’, ‘Kiel Canal’, ‘non-FOC’, ‘small size tankers’, ‘collision’, ‘vessels over 12 years old’ and ‘contact’ were found to be the most frequent items in forming of the rules. It is expected that the results of this study can be helpful for the relevant authorities to understand the accidents in this region.

🔖

Cite This Article

Sevgili, C., Kundakçı, B. (2025). Identifying the association levels of marine accidents involving tankers in the Baltic Sea region. Marine Science and Technology Bulletin, Volume: 14 Issue: 4. https://doi.org/10.33714/masteb.1802522

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (36)

  1. Agrawal R. & Srikant R. (1994). Fast Algorithms for Mining Association Rules. In J. B. Bocca, M. Jarke & C. Zaniolo (Eds.), VLDB '94: Proceedings of the 20th International Conference on Very Large Data Bases (pp. 487-499). Morgan Kaufmann Publishers Inc.
  2. Agrawal, R., Imielinski, T. & Swami, A. (1993). Mining association rules between sets of items in large databases. In P. Buneman & S. Jajodia (Eds.), SIGMOD '93: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (pp. 207–216). Association for Computing Machinery. https://doi.org/10.1145/170035.170072
  3. Andersen, T., Hove, J. H., Fagerholt, K., & Meisel, F. (2021). Scheduling ships with uncertain arrival times through the Kiel Canal. Maritime Transport Research, 2, 100008. https://doi.org/10.1016/j.martra.2020.100008
  4. Angeline, D. M. D. (2013). Association rule generation for student performance analysis using apriori algorithm. The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), 1(1), 12-16.
  5. Çakır, E., Fışkın, R., & Sevgili, C. (2021). Investigation of tugboat accidents severity: An application of association rule mining algorithms. Reliability Engineering & System Safety, 209, 107470. https://doi.org/10.1016/j.ress.2021.107470
  6. Cao, Y., Wang, X., Wang, Y., Fan, S., Wang, H., Yang, Z., Liu, Z., Wang, J., & Shi, R. (2023). Analysis of factors affecting the severity of marine accidents using a data-driven Bayesian network. Ocean Engineering, 269, 113563. https://doi.org/10.1016/j.oceaneng.2022.113563
  7. Chung, W. H., Kao, S. L., Chang, C. M., & Yuan, C. C. (2020). Association rule learning to improve deficiency inspection in port state control. Maritime Policy & Management, 47(3), 332–351. https://doi.org/10.1080/03088839.2019.1688877
  8. de Silva, R., Stanton, P., & Stanton, J. (2011). Determinants of Indian sub-continent officer–seafarer retention in the shipping industry. Maritime Policy & Management, 38(6), 633-644. https://doi.org/10.1080/03088839.2011.615869
  9. Dongre, J., Prajapati, G. L., & Tokekar, S. V. (2014). The role of Apriori algorithm for finding the association rules in Data mining. International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), India, pp. 657-660. https://doi.org/10.1109/ICICICT.2014.6781357
  10. Eliopoulou, E., Papanikolaou, A., & Voulgarellis, M. (2016). Statistical analysis of ship accidents and review of safety level. Safety Science, 85, 282-292. https://doi.org/10.1016/j.ssci.2016.02.001
  11. EMSA (European Maritime Safety Agency). (2024). Annual Overview of Marine Casualties and Incidents 2024. European Maritime Safety Agency. Retrieved on October, 07, 2025 from https://www.emsa.europa.eu/publications/reports/item/5352-annual-overview-of-marine-casualties-and-incidents-2024.html
  12. European Marine Observation and Data Network (EMODnet). (2025). EMODnet Map Viewer. European Marine Observation and Data Network. Retrieved on October, 07, 2025 from https://emodnet.ec.europa.eu/geoviewer
  13. Häkkinen, J., & Posti, A. (2013). Overview of maritime accidents involving chemicals worldwide and in the Baltic Sea. In A. Weintrit & T. Neumann (Eds.), Marine navigation and safety of sea transportation: Maritime transport & shipping – Pollution at sea, cargo safety, environment protection and ecology (pp. 15-25). CRC Press.
  14. Han, J., Kamber, M., & Pei, J. (2011). Data mining: Concepts and techniques (The Morgan Kaufmann series in data management systems). 3rd ed. Morgan Kaufmann, Elsevier.
  15. Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2009). The elements of statistical learning: Data mining, inference and prediction. Second Edition. Springer. https://doi.org/10.1007/978-0-387-84858-7
  16. Hikmawati, E., Maulidevi, N. U., & Surendro, K. (2021). Minimum threshold determination method based on dataset characteristics in association rule mining. Journal of Big Data, 8, 146. https://doi.org/10.1186/s40537-021-00538-3
  17. Huang, D., Liang, T., Hu, S., Loughney, S., & Wang, J. (2023). Characteristics analysis of intercontinental sea accidents using weighted association rule mining: Evidence from the Mediterranean Sea and Black Sea. Ocean Engineering, 287(Part 2), 115839. https://doi.org/10.1016/j.oceaneng.2023.115839
  18. International Maritime Organization (IMO). (2005). Resolution MEPC.136(53) Designation of the Baltic Sea Area as a Particularly Sensitive Sea Area. International Maritime Organization
  19. Kaur, G., & Madan, N. (2015). Association rule mining: A survey. International Journal of Hybrid Information Technology, 8(7), 239–242. https://doi.org/10.14257/ijhit.2015.8.7.22
  20. Kulkarni, K., Goerlandt, F., Li, J., Banda, O. V., & Kujala, P. (2020). Preventing shipping accidents: Past, present, and future of waterway risk management with Baltic Sea focus. Safety Science, 129, 104798. https://doi.org/10.1016/j.ssci.2020.104798
  21. Kumbhare, T. A., & Chobe, S. V. (2014). An overview of association rule mining algorithms. International Journal of Computer Science and Information Technologies, 5(1), 927-930.
  22. Kundakci, B., & Nas, S. (2025). Evaluation of the anchorage area selection by using association rule mining. Case Studies on Transport Policy, 21, 101544. https://doi.org/10.1016/j.cstp.2025.101544
  23. Lan, H., Ma, X., Ma, L., & Qiao, W. (2023). Pattern investigation of total loss maritime accidents based on association rule mining. Reliability Engineering & System Safety, 229, 108893. https://doi.org/10.1016/j.ress.2022.108893
  24. Lu, H., Feng, L., & Han, J. (2000). Beyond intratransaction association analysis: Mining multidimensional intertransaction association rules. ACM Transactions on Information Systems, 18(4), 423–454. https://doi.org/10.1145/358108.358114
  25. Mlakar, U., Zorman, M., Fister Jr, I., & Fister, I. (2017). Modified binary cuckoo search for association rule mining. Journal of Intelligent & Fuzzy Systems, 32(6), 4319-4330. https://doi.org/10.3233/JIFS16963
  26. Nahar, J., Imam, T., Tickle, K. S., & Chen, Y. P. P. (2013). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40(4), 1086-1093. https://doi.org/10.1016/j.eswa.2012.08.028
  27. Papanikolaou, A., & Eliopoulou, E. (2008). Impact of ship age on tanker accidents. Proceedings of the 2nd International Symposium on “Ship Operations, Management and Economics”, The Greek Scetion of the Society of Naval Architects and Marine Engineers (SNAME), Greece, pp. 1-8.
  28. Sevgi̇li̇, C. (2024a). Data-driven prediction model for pollution prevention deficiencies on ships. Regional Studies in Marine Science, 78, 103790. https://doi.org/10.1016/j.rsma.2024.103790
  29. Sevgili, C. (2024b). Evaluation of pollution prevention related deficiencies of ships using association rule mining. Marine Pollution Bulletin, 208, 116938. https://doi.org/10.1016/j.marpolbul.2024.116938
  30. Sevgili, C., Cakir, E., & Fiskin, R. (2025). Maritime security threats: Classifying and associating patterns in piracy and armed robbery incidents. Ocean & Coastal Management, 266, 107685. https://doi.org/10.1016/j.ocecoaman.2025.107685
  31. Szubrycht, T. (2020). Marine accidents as potential crisis situations on the Baltic Sea. Archives of Transport, 54(2), 125-135. https://doi.org/10.5604/01.3001.0014.2972
  32. United Nations Conference on Trade and Development (UNCTAD). (2025). Review of Maritime Transport 2025: Staying the course in turbulent waters. United Nations Publications.
  33. Wang, H., Liu, Z., Wang, X., Graham, T., & Wang, J. (2021). An analysis of factors affecting the severity of marine accidents. Reliability Engineering & System Safety, 210, 107513. https://doi.org/10.1016/j.ress.2021.107513
  34. Yuan, X. (2017). An improved Apriori algorithm for mining association rules. AIP Conference Proceedings, 1820, 080005. https://doi.org/10.1063/1.4977361
  35. Zhang, Y., Zhai, Y., Chen, J., Xu, Q., Fu, S., & Wang, H. (2022). Factors contributing to fatality and injury outcomes of maritime accidents: A comparative study of two accident-prone areas. Journal of Marine Science and Engineering, 10(12), 1945. https://doi.org/10.3390/jmse10121945
  36. Zhou, K., Xing, W., Wang, J., Li, H. & Yang, Z. (2024). A data-driven risk model for maritime casualty analysis: A global perspective. Reliability Engineering & System Safety, 244, 109925. https://doi.org/10.1016/j.ress.2023.109925