Towards a probabilistic model for predicting ship besetting in ice in Arctic waters

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • Inria Université Paris-Saclay
  • Wuhan University of Technology
  • Finnish Geospatial Research Institute
  • Gdynia Maritime University
  • National Engineering Research Center for Water Transport Safety
  • Polytechnic University of Milan

Abstract

Recently, the melting of sea ice due to global warming has made it possible for merchant ships to navigate through Arctic Waters. However, Arctic Marine Transportation System remains a very demanding, dynamic and complex system due to challenging hydro-meteorological conditions, poorly charted waters and remoteness of the area resulting in lack of appropriate response capacity in case of emergency. In order to ensure a proper safety level for operations such as ship transit within the area, a risk analysis should be carried out, where the relevant factors pertaining to a given operation are defined and organized in a model. Such a model can assist onshore managers or ships’ crews in planning and conducting an actual sea passage through Arctic waters. However, research in this domain is scarce, mainly due to lack of data. In this paper, we demonstrate the use of a dataset and expert judgment to determine the risk influencing factors and develop a probabilistic model for a ship besetting in ice along the Northeast Passage. For that purpose, we adopt Bayesian belief Networks (BBNs), due to their predominant feature of reasoning under uncertainty and their ability to accommodate data from various sources. The obtained BBN model has been validated showing good agreement with available state-of-the-art models, and providing good understanding of the analyzed phenomena.

Details

Original languageEnglish
Pages (from-to)124-136
Number of pages13
JournalReliability Engineering and System Safety
Volume155
Publication statusPublished - 1 Nov 2016
MoE publication typeA1 Journal article-refereed

    Research areas

  • Bayesian belief networks, Probabilistic risk assessment, Ship performance in Arctic waters, Ship stuck in ice

ID: 6737017