6,177
Views
24
CrossRef citations to date
0
Altmetric
Articles

A survey of the opportunities and challenges of supervised machine learning in maritime risk analysis

ORCID Icon & ORCID Icon
Pages 108-130 | Received 21 Jun 2021, Accepted 25 Jan 2022, Published online: 07 Feb 2022

References

  • Adland, R., Jia, H., Lode, T., & Skontorp, J. (2021). The value of meteorological data in marine risk assessment. Reliability Engineering and System Safety, 209. doi:10.1016/j.ress.2021.107480
  • Allianz. (2012). Safety and shipping 1912–2012: From Titanic to Costa Concordia [online]. Accessed 18th May 2020. https://www.agcs.allianz.com/content/dam/onemarketing/agcs/agcs/reports/AGCS-Safety-Shipping-Review-2012.pdf
  • Aven, T., & Heide, B. (2009). Reliability and validity of risk analysis. Reliability Engineering and System Safety, 94, 1862–1868. doi:10.1016/j.ress.2009.06.003
  • Baksh, A., Abbassi, R., Garaniya, V., & Khan, F. (2018). Marine transportation risk assessment using Bayesian Network: Application to Arctic waters. Ocean Engineering, 159, 422–436. doi:10.1016/j.oceaneng.2018.04.024
  • Blanc, L., Hashemi, R., & Rucks, C. (2001). Pattern development for vessel accidents: A comparison of statistical and neural computing techniques. Expert Systems with Applications, 20, 163–171. doi:10.1016/S0957-4174(00)00056-7
  • Bowen, H. (2020). The shipping loses of the British East India Company, 1750–1813. International Journal of Maritime History, 32(2), 323–336. doi:10.1177/0843871420920963
  • Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. doi:10.1023/A:1010933404324
  • Bye, R., & Aalberg, A. (2018). Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports. Reliability Engineering and System Safety, 176, 174–186. doi:10.1016/j.ress.2018.03.033
  • Bye, R., & Almklov, P. (2019). Normalization of maritime accident data using AIS. Marine Policy, 109. doi:10.1016/j.marpol.2019.103675
  • Chai, T., Xue, H., Sun, K., & Weng, J. (2020). Ship accident prediction based on improved quantum-behaved PSO-LSSVM. Mathematical Problems in Engineering, 2020. doi:10.1155/2020/8823322
  • Chang, Y., & Park, H. (2019). The impact of vessel speed reduction on port accidents. Accident Analysis and Prevention, 123, 422–432. doi:10.1016/j.aap.2016.03.003
  • Chawla, N., Bowyer, K., Hall, L., & Keglemeyer, W. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. doi:10.1613/jair.953
  • Chen, C., Chen, X., Ma, F., Zeng, X., & Wang, J. (2019). A knowledge-free path planning approach for smart ships based on reinforcement learning. Ocean Engineering, 189. doi:10.1016/j.oceaneng.2019.106299
  • Chen, P., Huang, Y., Mou, J., & van Gelder, P. (2019). Probabilistic risk analysis for ship-ship collision: State of the art. Safety Science, 117, 108–122. doi:10.1016/j.ssci.2019.04.014
  • Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. San Francisco: KDD. doi:10.1145/2939672.2939785
  • Chen, Z., Xue, J., Wu, C., Qin, L., Liu, L., & Cheng, X. (2018). Classification of vessel motion pattern in inland waterways based on automatic identification system. Ocean Engineering, 161, 69–76. doi:10.1016/j.oceaneng.2018.04.072
  • Coraddu, A., Oneto, L., Maya, B., & Kurt, R. (2020). Determining the most influential human factors in maritime accidents: A data-driven approach. Ocean Engineering, 211. doi:10.1016/j.oceaneng.2020.107588
  • Dorsey, C., Wang, B., Grabowski, M., Merrick, J., & Harrald, J. (2020). Self-healing databases for predictive risk analytics in safety-critical systems. Journal of Loss Prevention in the Process Industries, 63. doi:10.1016/j.jlp.2019.104014
  • Du, L., Goerlandt, F., & Kujala, P. (2020). Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data. Reliability Engineering and System Safety, 200. doi:10.1016/j.ress.2020.106933
  • EMSA. (2018). Joint workshop on risk assessment and response planning in Europe. London.
  • EMSA. (2019). Annual overview of marine casualties and incidents 2019 [online]. Accessed 18th May 2020. http://www.emsa.europa.eu/emsa-homepage/2-news-a-press-centre/news/3734-annual-overview-of-marine-casualties-and-incidents-2019.html
  • Fan, L., Wang, M., & Yin, J. (2019). The impacts of risk level based on PSC inspection deficiencies on ship accident consequences. Research in Transportation Business and Management, 33. doi:10.1016/j.rtbm.2020.100464
  • Fan, L., Zhang, Z., Yin, J., & Wang, X. (2019). The efficiency improvements of port state control based on ship accident Bayesian networks. Proceedings of the Institution of Mechanical Engineers, Port O: Journal of Risk and Reliability, 233(1), 71–83. doi:10.1177/1748006X18811199
  • Fan, S., Yang, Z., Blanco-Davis, E., Zhang, J., & Yan, X. (2020). Analysis of maritime transport accidents using Bayesian Networks. Proceedings of the Institute of Mechanical Engineers Part O: Journal of Risk and Reliability, 234(3), 439–454. doi:10.1177/1748006X19900850
  • Gang, L., Wang, Y., Sun, Y., Zhou, L., & Zhang, M. (2016). Estimation of vessel collision risk index based on support vector machine. Advances in Mechanical Engineering, 8(11), 1–10. doi:10.1177/1687814016671250
  • Goerlandt, F., & Kujala, P. (2014). On the reliability and validity of ship-ship collision risk analysis in light of different perspectives on risk. Safety Science, 62, 348–365. doi:10.1016/j.ssci.2013.09.010
  • Goerlandt, F., & Montewka, J. (2015). Maritime transportation risk analysis: Review and analysis in light of some foundational issues. Reliability Engineering & System Safety, 138, 115–134. doi:10.1016/j.ress.2015.01.025
  • Guikema, S. (2020). Artificial intelligence for natural hazards risk analysis: Potential, challenges, and research needs. Risk Analysis, 40, 1117–1123. doi:10.1111/risa.13476
  • Harati-Mokhtari, A., Brooks, P., Wall, A., & Wang, J. (2007). Automatic Identification System (AIS): Data reliability and human error implications. Journal of Navigation, 60, 373–389. doi:10.1017/S0373463307004298
  • Hashemi, R., Blanc, L., Rucks, C., & Shearry, A. (1995). A neural network for transportation safety modelling. Expert Systems with Applications, 9(3), 247–256.
  • Hassel, M., Asbjornslett, B., & Hole, L. (2011). Underreporting of maritime accidents to vessel accident databases. Accident Analysis and Prevention, 43, 2053–2063. doi:10.1016/j.aap.2011.05.027
  • He, J., Hao, Y., & Wang, X. (2021). An interpretable aid decision-making model for flag state control ship detention based on SMOTE and XGBoost. Journal of Marine Science and Technology, 9, 2. doi:10.3390/jmse9020156
  • Hedge, J., & Rokseth, B. (2020). Applications of machine learning methods for engineering risk assessment – A review. Safety Science, 122. doi:10.1016/j.ssci.2019.09.015
  • Heij, C., & Knapp, S. (2018). Predictive power of inspection outcomes for future shipping accidents – An empirical appraisal with special attention for human factor aspects. Maritime Policy and Management, 45(5), 604–621. doi:10.1080/03088839.2018.1440441
  • Hoorn, S., & Knapp, S. (2015). A multi-layered risk exposure assessment approach for the shipping industry. Transportation Research Part A, 78, 21–33. doi:10.1016/j.tra.2015.04.032
  • IALA. (2002). IALA guidelines on the universal automatic identification system (AIS). Volume 1, Part II – Technical Issues. Edition 1.1.
  • Iphar, C., Ray, C., & Napoli, A. (2020). Data integrity assessment for maritime anomaly detection. Expert Systems with Applications, 147. doi:10.1016/j.eswa.2020.113219
  • ITOPF. (2020). Oil tanker spill statistics 2019 [online]. Accessed 18th May 2020. https://www.itopf.org/knowledge-resources/data-statistics/statistics/.
  • Jin, M., Shi, W., Lin, K., & Li, K. (2019). Marine piracy prediction and prevention: Policy implications. Marine Policy, 108. doi:10.1016/j.marpol.2019.103528
  • Jin, M., Shi, W., Yuen, K., Xiao, Y., & Li, K. (2019). Oil tanker risks on the marine environment: An empirical study and policy implications. Marine Policy, 108. doi:10.1016/j.marpol.2019.103655
  • Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgement under uncertainty: Heuristics and biases. Cambridge: Cambridge University Press.
  • Karakasnaki, M., Vlachopoulos, P., Pantouvakis, A., & Bouranta, N. (2018). ISM code implementation: An investigation of safety issues in the shipping industry. WMU Journal of Maritime Affairs, 17, 461–474. doi:10.1007/s13437-018-0153-4
  • Kecman, V. (2005). Support vector machines – An introduction. In L. Wang (Ed.), Support vector machines: Theory and applications (pp. 1–48). New York: Springer.
  • Knapp, S., & Franses, P. (2008). Econometric analysis to differentiate effects of various ship safety inspections. Marine Policy, 32(4), 653–662. doi:10.1016/j.marpol.2007.11.006
  • Knapp, S., & Heij, C. (2020). Improve strategies for the maritime industry to target vessels for inspection and to select inspection priority areas. Safety, 6, 2. doi:10.3390/safety6020018
  • Knapp, S., Kumar, S., Sakurada, Y., & Shen, J. (2011). Econometric analysis of the changing effects in wind strength and significant wave height on the probability of casualty in shipping. Accident Analysis and Prevention, 43, 1252–1266. doi:10.1016/j.aap.2011.01.008
  • Kretschmann, L. (2020). Leading indicators and maritime safety: Predicting future risk with a machine learning approach. Journal of Shipping and Trade, 5. doi:10.1186/s41072-020-00071-1
  • Kulkarni, K., Goerlandt, F., Li, J., Banda, O., & Kujala, P. (2020). Preventing shipping accidents: Past, present and future of waterway risk management with Baltic Sea focus. Safety Science, 129.doi:10.1016/j.ssci.2020.104798
  • Leevy, J., Khoshgoftaar, T., Bauder, R., & Seliya, N. (2018). A survey on addressing high-class imbalance in big data. Journal of Big Data, 5, 42. doi:10.1186/s40537-018-0151-6
  • Lensu, M., & Goerlandt, F. (2019). Big maritime data for the Baltic Sea with a focus on the winter navigation system. Marine Policy, 104, 53–65. doi:10.1016/j.marpol.2019.02.038
  • Li, J., Goerlandt, F., & Reniers, G. (2021). An overview of scientometric mapping for the safety science community: Methods, tools, and framework. Safety Science, 134. doi:10.1016/j.ssci.2020.105093
  • Li, K., Yin, J., Bang, H., Yang, Z., & Wang, J. (2012). Bayesian network with quantitative input for maritime risk analysis. Transportmetrica A: Transport Science, 10(2), 89–118. doi:10.1080/18128602.2012.675527
  • Li, K., Yin, J., & Fan, L. (2014). Ship safety index. Transportation Research Part A, 66, 75–87. doi:10.1016/j.tra.2014.04.016
  • Li, S., Meng, Q., & Qu, X. (2012). An overview of maritime waterway quantitative risk assessment models. Risk Analysis, 32(3), 496–512. doi:10.1111/j.1539-6924.2011.01697.x
  • Likun, W., & Zaili, Y. (2018). Bayesian networking modelling and analysis of accident severity in waterborne transportation: A case study in China. Reliability Engineering and System Safety, 180, 277–289. doi:10.1016/j.ress.2018.07.021
  • Lim, G., Cho, J., Bora, S., Biobaku, T., & Parsaei, H. (2018). Models and computational algorithms for maritime risk analysis: A review. Annals of Operational Research, 271, 765–786. doi:10.1007/s10479-018-2768-4
  • Lisowski, J. (2016). Dynamic optimisation of safe ship trajectory with neural representation of encountered ships. Scientific Journals of the Maritime University of Szczecin, 47, 91–97.
  • Liu, D., Wang, X., Cai, Y., Liu, Z., & Liu, Z. (2020). A novel framework of real time regional collision risk prediction based on the RNN approach. Journal of Marine Science and Engineering, 8(3), 224. doi:10.3390/jmse8030224
  • Liu, Y., Duan, W., Huang, L., Duan, S., & Ma, X. (2020). The input vector space optimisation for LSTM deep learning model in real-time prediction of ship motions. Ocean Engineering, 213. doi:10.1016/j.oceaneng.2020.107681
  • Ma, J., Li, W., Jia, C., Zhang, C., & Zhang, Y. (2020). Risk prediction for ship encounter situation awareness using long short-term memory based deep learning on internship behaviours. Journal of Advanced Transportation. doi:10.1155/2020/8897700
  • Mazaheri, A., Montewka, J., Kotilainen, P., Sormunen, O. E., & Kujala, P. (2014). Assessing grounding frequency using ship traffic and waterway complexity. Journal of Navigation, 68(1), 89–106. doi:10.1017/S0373463314000502
  • Mazaheri, A., Montewka, J., & Kujala, P. (2016). Towards an evidence-based probabilistic risk model for ship-grounding accidents. Safety Science, 86, 195–210. doi:10.1016/j.ssci.2016.03.002
  • MCA. (2010). The human element: A guide to human behaviour in the shipping industry. The Stationary Office, London.
  • McCulloch, W., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133. doi:10.1007/BF02478259
  • Mehdizadeh, A., Cai, M., Hu, Q., Yazdi, M., Mohabbati-Kalejahi, N., Vinel, A., … Megahed, F. (2020). A review of data analytic applications in road traffic safety. Part 1: Descriptive and predictive modelling. Sensors, 20(4), 1107. doi:10.3390/s20041107
  • Merghadi, A., Yunus, A., Dou, J., Whiteley, J., ThaiPham, B., Tien Bui, D., … Abderrahmane, B. (2020). Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Science Reviews, 207, 103225. doi:10.1016/j.earscirev.2020.103225
  • Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghazali, A. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 8(2), 1080–1102. doi:10.1080/19475705.2017.1294113
  • Norrington, L., Quigley, J., Russell, A., & Meer, R. (2008). Modelling the reliability of search and rescue operations with Bayesian Belief Networks. Reliability Engineering and System Safety, 93(7), 940–949. doi:10.1016/j.ress.2007.03.006
  • OpenRisk. (2018). OpenRisk guideline for regional risk management to improve European pollution preparedness and response at sea [online]. Accessed 17th October 2020. https://helcom.fi/media/publications/OpenRisk-Guideline-for-pollution-response-at-sea.pdf.
  • Ozturk, U., Birbil, S., & Kadir, C. (2019). Evaluating navigational risk of port approach manoeuvrings with expert assessments and machine learning. Ocean Engineering, 192. doi:10.1016/j.oceaneng.2019.106558
  • Paltrinieri, N., Comfort, L., & Reniers, G. (2019). Learning about risk: Machine learning for risk assessment. Safety Science, 118, 475–486. doi:10.1016/j.ssci.2019.06.001
  • Pietrzykowski, Z. (2001). The analysis of a ship fuzzy domain in a restricted area. IFAC Proceedings Volumes, 34(7), 45–50. doi:10.1016/S1474-6670(17)35057-7
  • Pietrzykowski, Z., Wielgosz, M., & Breitsprecher, M. (2020). Navigators behaviour analysis using data mining. Journal of Marine Science and Engineering, 8, 1. doi:10.3390/jmse8010050
  • Pozzolo, A., Caelen, O., Johnson, R., & Bontempi, G. (2015). Calibrating probability with undersampling for unbalanced classification. IEEE Symposium Series on Computational Intelligence, Cape Town (pp. 159–166). doi:10.1109/SSCI.2015.33
  • Psaraftis, H. N. (2012). Formal safety assessment: An updated review. Journal of Marine Science and Technology, 11(3), 390–402. doi:10.1007/s00773-012-0175-0
  • Qiao, W., Liu, Y., Ma, X., & Liu, Y. (2020). A methodology to evaluate human factors contributed to maritime accident by mapping fuzzy FT into ANN based on HFACS. Ocean Engineering, 197. doi:10.1016/j.oceaneng.2019.106892
  • Rawson, A., & Brito, M. (2021a). Developing contextually aware ship domains using machine learning. Journal of Navigation. doi:10.1017/S0373463321000047
  • Rawson, A., & Brito, M. (2021b). A critique of the use of domain analysis for spatial collision risk assessment. Ocean Engineering, 219. doi:10.1016/j.oceaneng.2020.108259
  • Razavi, A., Inkpen, D., Falcon, R., & Abielmona, R. (2014). Textual risk mining for maritime situational awareness. IEEE International Inter-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support. doi:10.1109/CogSIMA.2014.6816558
  • Rezaee, S., Pelot, R., & Finnis, J. (2016). The effect of extratropical cyclone weather conditions on fishing vessel incidents’ severity level in Atlantic Canada. Safety Science, 85, 33–40. doi:10.1016/j.ssci.2015.12.006
  • Rezaee, S., Pelot, R., & Ghasemi, A. (2016). The effect of extreme weather conditions on commercial fishing activities and vessel incidents in Atlantic Canada. Ocean and Coastal Management, 130, 115–127. doi:10.1016/j.ocecoaman.2016.05.011
  • Riveiro, M., Pallotta, G., & Vespe, M. (2018). Maritime anomaly detection: A review. Data Mining and Knowledge Discovery, 8, 5. doi:10.1002/widm.1266
  • Tang, L., Tang, Y., Zhang, K., Du, L., & Wang, M. (2019). Prediction of grades of ship collision accidents based on random forests and bayesian networks. 5th International Conference on Transportation Information and Safety, July 14–17, Liverpool.
  • Tsou, M. (2018). Big data analytics of safety assessment for a port of entry: A case study in Keelung Harbour. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 233(4), 1260–1275. doi:10.1177/1475090218805245
  • Uyanik, T., Karatug, C., & Arslanoglu, Y. (2020). Machine learning approach to ship fuel consumption: A case study of container vessel. Transportation Research Part D, 84. doi:10.1016/j.trd.2020.102389
  • Wang, C., Deng, C., & Wang, S. (2020). Imbalance-XGBoost: Leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost. Pattern Recognition Letters, 136, 190–197. doi:10.1016/j.patrec.2020.05.035
  • Wang, H., Liu, Z., Wang, X., Graham, T., & Wang, J. (2021). An analysis of factors affecting the severity of marine accidents. Reliability Engineering and System Safety, 210. doi:10.1016/j.ress.2021.107513
  • Wang, J., Sii, H., Yang, J., Pillay, A., Yu, D., Liu, J., & Saajedi, A. (2004). Use of advances in technology for maritime risk assessment. Risk Analysis, 24(4), 1041–1063. doi:10.1111/j.0272-4332.2004.00506.x
  • Wang, L., Huang, R., Shi, W., & Zhang, C. (2021). Domino effect in marine accidents: Evidence from temporal association rules. Transport Policy, 103, 236–244. doi:10.1016/j.tranpol.2021.02.006
  • Wang, Y., Wang, L., Jiang, J., Wang, J., & Yang, Z. (2020). Modelling ship collision risk based on the statistical analysis of historical data: A case study in Hong Kong waters. Ocean Engineering, 197. doi:10.1016/j.oceaneng.2019.106869
  • Wen, X., Xie, Y., Jiang, L., Pu, Z., & Ge, T. (2021). Applications of machine learning methods in traffic crash severity modelling: Current status and future directions. Transport Reviews. doi:10.1080/01441647.2021.1954108
  • Wu, S., Chen, X., Shi, C., Fu, J., Yan, Y., & Wang, S. (2021). Ship detention prediction via feature selection scheme and support vector machine (SVM). Maritime Policy and Management. doi:10.1080/03088839.2021.1875141
  • Wu, Y., Pelot, R., & Hilliard, C. (2009). The influence of weather conditions on the relative incident rate of fishing vessels. Risk Analysis, 29(7), 985–999. doi:10.1111/j.1539-6924.2009.01217.x
  • Xiao, Y., Wang, G., Lin, K., Qi, G., & Li, K. (2020). The effectiveness of the new inspection regime for port state control: Application of the Tokyo MoU. Marine Policy, 115. doi:10.1016/j.marpol.2020.103857
  • Yang, D., Wu, L., Wang, S., Jia, H., & Li, K. (2019). How big data enriches maritime research – A critical review of Automatic Identification System (AIS) data applications. Transport Reviews, 39(6), 755–773. doi:10.1080/01441647.2019.1649315
  • Yang, Z., Yang, Z., & Yin, J. (2018). Realising advanced risk-based port state control inspection using data-driven Bayesian networks. Transportation Research Part A, 110, 38–56. doi:10.1016/j.tra.2018.01.033
  • Yuan, Z., Zhou, X., Yang, T., Tamerius, J., & Mantilla, R. (2017). Predicting traffic accidents through heterogeneous urban data: A case study. 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Halifax. doi:10.475/123_4
  • Zhang, W., Feng, X., Goerlandt, F., & Liu, Q. (2020). Towards a convolutional neural network model for classifying regional ship collision risk levels for waterway risk analysis. Reliability Engineering and System Safety, 204. doi:10.1016/j.ress.2020.107127
  • Zhang, Y., Sun, X., Chen, J., & Cheng, C. (2021). Spatial patterns and characteristics of global maritime accidents. Reliability Engineering and System Safety, 206, 107310. doi:10.1016/j.ress.2020.107310
  • Zheng, K., Chen, Y., Jiang, Y., & Qiao, S. (2020). A SVM based ship collision risk assessment algorithm. Ocean Engineering, 202. doi:10.1016/j.oceaneng.2020.107062