Show simple item record

dc.contributor.authorKim, Ekaterina
dc.contributor.authorSmestad, Bjørnar Brende
dc.contributor.authorAsbjørnslett, Bjørn Egil
dc.date.accessioned2021-01-25T08:45:00Z
dc.date.available2021-01-25T08:45:00Z
dc.date.created2020-09-28T16:07:07Z
dc.date.issued2020
dc.identifier.isbn978-1-880653-84-5
dc.identifier.urihttps://hdl.handle.net/11250/2724429
dc.description.abstractThis 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.en_US
dc.language.isoengen_US
dc.publisherInternational Society of Offshore & Polar Engineersen_US
dc.relation.ispartofProceedings of the Thirtieth (2020) International Ocean and Polar Engineering Conference Shanghai, China, October 11-16, 2020
dc.titlePredicting ship speeds in the Arctic using deep learning on historical AIS dataen_US
dc.typeChapteren_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber622-626en_US
dc.identifier.cristin1834444
dc.relation.projectNotur/NorStore: NS9672Ken_US
dc.description.localcodeThis chapter will not be available due to copyright restrictions (c) 2020 by International Society of Offshore & Polar Engineersen_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record