Arctic climate is changing. Areas covered by sea ice are decreasing, while the number of vessels operating in ice infested water increases. This study offers a perspective of applying fully connected neural networks (NN) to predict vessels' speeds on a part of the Northern Sea Route and discusses its challenges. A fully connected neural network model was used to predict vessels speeds in the Eastern Barents Sea region and the Southern Kara Sea region. The results demonstrate the ability of the model to predict the vessel's speed based on its geographical location, time of the voyage, vessel purpose, size and ice class. The model performance was verified against randomly selected AIS (Automatic Identification System) data that were enhanced using information of the Northern Sea Route administration and of the Vessel Finder database. Testing of the model on three individual vessel transits demonstrated good results in terms of predicting general speed trends during the transits. Furthermore, we have identified two challenges in applying a fully connected NN to speed regime modelling: data quality and accessibility. These challenges are discussed and techniques to minimize them are presented in this paper. Being familiar with the advantages and limitations of fully connected NN in the modelling of vessels speeds is essential to leverage its predictive capabilities, with the goal of improving safety, emergency, and transport planning of Arctic voyages.


Norway manages oceans that are five to six times larger than it's terrestrial areas, and since much of this is in the Arctic, Norway has a global responsibility for sustainability and knowledge-based management of the Arctic oceans. As the demand upon shipping in the high north are likely to increase in the future, a more in-depth understanding and modelling of the speed regimes in the Arctic become increasingly more important.

The need to address the subject of vessels’ speeds in ice exposed areas stems from two conditions: the world-wide increase of marine activity in ice-covered waters accompanied by climate change, electrification, digitalization, automation, and the rather wide disparities on how to predict the speeds for planning of Arctic transportations and for estimation of the emergency response time.

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