Probability of a ship becoming beset in ice along the Northern Sea Route -- a Bayesian analysis of real-life data

Jarno Vanhatalo, Juri Huuhtanen, Martin Bergström, Inari Helle, Jussi Antti-Eerikki Mäkinen, Pentti Kujala

Research output: Contribution to journalArticleScientificpeer-review


Ships operating in ice-infested Arctic waters are exposed to a range of ship-ice interaction related hazards. One of the most dangerous of these is the possibility of a ship becoming beset in ice, meaning that a ship is surrounded by ice preventing it from maneuvering under its own power. Such a besetting event may not only result in severe operational disruption, but also expose a ship to severe ice loading or cause it to drift towards shallow water. This may cause significant structural damage to a ship and potentially jeopardize its safety. To support safe and sustainable Arctic shipping operations, this article presents a probabilistic approach to assess the probability of a ship becoming beset in ice. To this end, the proposed approach combines different types of data, including Automatic Identification System (AIS) data, satellite ice data, as well as data on real-life ship besetting events. Based on this data, using a hierarchical Bayesian model, the proposed approach calculates the probability of a besetting event as a function of the Polar Ship Category of a ship, sea area, and the distance travelled in the prevailing ice concentration. The utility of the proposed approach, e.g. in supporting spatiotemporal risk assessments of Arctic shipping activities as well as Arctic voyage planning, is demonstrated through a case study in which the approach is applied to ships operating in the Northern Sea Route (NSR) area. The outcomes of the case study indicate that the probability of besetting is strongly dependent on the Polar Ship Category of a ship and that the probability increases significantly with higher ice concentrations. The sea area, on the other hand, does not appear to significantly affect the probability of besetting.

Original languageEnglish
Article number103238
JournalCold Regions Science and Technology
Number of pages9
Publication statusPublished - Apr 2021
MoE publication typeA1 Journal article-refereed

Fields of Science

  • Arctic
  • Automatic Identification System (AIS)
  • Bayesian inference
  • Ice besetting
  • Ice data
  • Ice loads
  • Maritime traffic
  • Northern Sea Route
  • Polar
  • Shipping
  • Ships
  • 218 Environmental engineering
  • 1171 Geosciences

Cite this