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dc.contributor.authorZhang, Zhihua
dc.contributor.authorCrabbe, M. James C.
dc.date.accessioned2021-07-28T10:45:46Z
dc.date.available2021-07-27T00:00:00Z
dc.date.available2021-07-28T10:45:46Z
dc.date.issued2021-07-20
dc.identifier.citationZhang Z, Crabbe MJC (2021) 'Management of environmental streaming data to optimize Arctic shipping routes.', Arabian Journal of Geosciences, 14 (), pp.1441-.en_US
dc.identifier.issn1866-7511
dc.identifier.doi10.1007/s12517-021-07782-0
dc.identifier.urihttp://hdl.handle.net/10547/625061
dc.description.abstractDynamic accurate predictions of Arctic sea ice, ocean, atmosphere, and ecosystem are necessary for safe and efficient Arctic maritime transportation; however a related technical roadmap has not yet been established. In this paper, we propose a management system for trans-Arctic maritime transportation supported by near real-time streaming data from air-space-ground-sea integrated monitoring networks and high spatio-temporal sea ice modeling. As the core algorithm of integrated monitoring networks, a long short-term memory (LSTM) neural network is embedded to improve Arctic sea ice mapping algorithms.Since the LSTM is localized in time and space, it can make full use of streaming data characteristics. The sea ice–related parameters from satellite remote sensing raw data are used as the input of the LSTM, while streaming data from shipborne radar networks and/or buoy measurements are used as training datasets to enhance the accuracy and resolution of environmental streaming data from outputs of LSTM. Due to large size of streaming data, the proposed management system of trans-Arctic shipping should be built on a cloud distribution platform using existing wireless communications networks among vessels and ports. Our management system will be used by the ongoing European Commission Horizon 2020 Programme “ePIcenter.”en_US
dc.description.sponsorshipThis research was partially supported by the European Commission Horizon 2020 Programme “ePIcenter” and Taishan Distinguished Professorship Fund.en_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.urlhttps://link.springer.com/article/10.1007/s12517-021-07782-0en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectarcticen_US
dc.subjectfood grain shipmenten_US
dc.subjectcloud computingen_US
dc.titleManagement of environmental streaming data to optimize Arctic shipping routes.en_US
dc.typeArticleen_US
dc.identifier.eissn1866-7538
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.contributor.departmentShandong Universityen_US
dc.identifier.journalArabian Journal of Geosciencesen_US
dc.date.updated2021-07-28T10:08:41Z
dc.description.notePublished gold OA from first publication in hybrid journal.
refterms.dateFOA2021-07-28T10:45:47Z


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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International