The value of meteorological data in marine risk assessment

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

Reliability Engineering and System Safety, 2021, vol. 209, issue C

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.

Keywords: marine insurance; AIS; meteorological data; risk analysis; machine learning; boosted trees (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:209:y:2021:i:c:s0951832021000466

DOI: 10.1016/j.ress.2021.107480

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