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The value of meteorological data in marine risk assessment

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  • Adland, Roar
  • Jia, Haiying
  • Lode, Tønnes
  • Skontorp, Jørgen

Abstract

The objective of this paper is to investigate whether the use of meteorological data improves the prediction of marine incidents, as represented by marine insurance claims for a vessel's voyage, both on a stand-alone basis and when combined with vessel-specific features and ship tracking data from the Automated Identification System (AIS). Furthermore, the paper investigates whether predictive performance improves when using machine learning algorithms, such as logistic LASSO regression and eXtreme Gradient Boosted Trees over classical logistic models, and identify dependencies and interaction effects among the risk factors within the SHapley Additive exPlanations framework. The data sample includes weather and AIS data for 42,000 voyages in the North Pacific between January 2013 and August 2019. The results suggest that meteorological information adds value in claims prediction and that short-term complex interactions between the vessel and weather conditions impact marine risk. The research is important for the improved modelling of marine risk on the basis of high-frequency, high-resolution ship tracking and weather data.

Suggested Citation

  • Adland, Roar & Jia, Haiying & Lode, Tønnes & Skontorp, Jørgen, 2021. "The value of meteorological data in marine risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    • Handle: RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000466
    DOI: 10.1016/j.ress.2021.107480
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    1. Rahman, Md Samsur & Khan, Faisal & Shaikh, Arifusalam & Ahmed, Salim & Imtiaz, Syed, 2020. "A conditional dependence-based marine logistics support risk model," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    2. Dinis, D. & Teixeira, A.P. & Guedes Soares, C., 2020. "Probabilistic approach for characterising the static risk of ships using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    3. Kevin X. Li & Yulan Wang & Jie Min, 2009. "Quantitative analysis of materiality in marine insurance," Maritime Policy & Management, Taylor & Francis Journals, vol. 36(5), pages 437-455, October.
    4. Kujala, P. & Hänninen, M. & Arola, T. & Ylitalo, J., 2009. "Analysis of the marine traffic safety in the Gulf of Finland," Reliability Engineering and System Safety, Elsevier, vol. 94(8), pages 1349-1357.
    5. Heij, C. & Knapp, S., 2018. "Predictive power of inspection outcomes for future shipping accidents," Econometric Institute Research Papers EI2018-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Christiaan Heij & Sabine Knapp, 2018. "Predictive power of inspection outcomes for future shipping accidents – an empirical appraisal with special attention for human factor aspects," Maritime Policy & Management, Taylor & Francis Journals, vol. 45(5), pages 604-621, July.
    7. Khan, Bushra & Khan, Faisal & Veitch, Brian & Yang, Ming, 2018. "An operational risk analysis tool to analyze marine transportation in Arctic waters," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 485-502.
    8. Gino J. Lim & Jaeyoung Cho & Selim Bora & Taofeek Biobaku & Hamid Parsaei, 2018. "Models and computational algorithms for maritime risk analysis: a review," Annals of Operations Research, Springer, vol. 271(2), pages 765-786, December.
    9. Yue Wu & Ronald P. Pelot & Casey Hilliard, 2009. "The Influence of Weather Conditions on the Relative Incident Rate of Fishing Vessels," Risk Analysis, John Wiley & Sons, vol. 29(7), pages 985-999, July.
    10. Goerlandt, Floris & Montewka, Jakub, 2015. "Maritime transportation risk analysis: Review and analysis in light of some foundational issues," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 115-134.
    11. Bye, Rolf J. & Aalberg, Asbjørn L., 2018. "Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 174-186.
    12. Jean-Philippe Boucher & Michel Denuit & Montserrat Guillén, 2007. "Risk Classification for Claim Counts," North American Actuarial Journal, Taylor & Francis Journals, vol. 11(4), pages 110-131.
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