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dc.contributor.authorChivers, Benedict
dc.contributor.authorWallbank, John
dc.contributor.authorCole, Steven
dc.contributor.authorSebek, Ondrej
dc.contributor.authorStanley, Simon
dc.contributor.authorFry, Matthew
dc.contributor.authorLeontidis, Georgios
dc.date.accessioned2021-05-29T23:14:39Z
dc.date.available2021-05-29T23:14:39Z
dc.date.issued2020-09
dc.identifier.citationChivers , B , Wallbank , J , Cole , S , Sebek , O , Stanley , S , Fry , M & Leontidis , G 2020 , ' Imputation of missing sub-hourly precipitation data in a large sensor network : a machine learning approach ' , Journal of Hydrology , vol. 588 , 125126 . https://doi.org/10.1016/j.jhydrol.2020.125126en
dc.identifier.issn0022-1694
dc.identifier.otherPURE: 170482700
dc.identifier.otherPURE UUID: 3562c87e-d3f3-4fef-addb-3b7e2ef9a20f
dc.identifier.otherScopus: 85085739845
dc.identifier.otherORCID: /0000-0001-6671-5568/work/76211663
dc.identifier.urihttps://hdl.handle.net/2164/16578
dc.descriptionThis research was supported by a UKRI-NERC Constructing a Digital Environment Strategic Priority grant “Engineering Transformation for the Integration of Sensor Networks: A Feasibility Study” [NE/S016236/1 & NE/S016244/1].en
dc.format.extent12
dc.language.isoeng
dc.relation.ispartofJournal of Hydrologyen
dc.rights© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectMachine learningen
dc.subjectData imputationen
dc.subjectEnvironmental sensor networksen
dc.subjectPrecipitationen
dc.subjectSoil moistureen
dc.subjectGradient boosted treesen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectEnvironmental Science (miscellaneous)en
dc.subjectArtificial Intelligenceen
dc.subjectComputer Science Applicationsen
dc.subjectWater Science and Technologyen
dc.subjectNatural Environment Research Council (NERC)en
dc.subjectNE/S016236/1en
dc.subjectNE/S016244/1en
dc.subject.lccQA75en
dc.titleImputation of missing sub-hourly precipitation data in a large sensor network : a machine learning approachen
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Computer Science and Informaticsen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Centre for Energy Transitionen
dc.description.statusPeer revieweden
dc.description.versionPostprinten
dc.identifier.doihttps://doi.org/10.1016/j.jhydrol.2020.125126
dc.date.embargoedUntil2021-05-30
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85085739845&partnerID=8YFLogxKen


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