Imputation of missing sub-hourly precipitation data in a large sensor network : a machine learning approach
| dc.contributor.author | Chivers, Benedict | |
| dc.contributor.author | Wallbank, John | |
| dc.contributor.author | Cole, Steven | |
| dc.contributor.author | Sebek, Ondrej | |
| dc.contributor.author | Stanley, Simon | |
| dc.contributor.author | Fry, Matthew | |
| dc.contributor.author | Leontidis, Georgios | |
| dc.contributor.institution | University of Aberdeen.Computing Science | en |
| dc.contributor.institution | University of Aberdeen.Centre for Energy Transition | en |
| dc.contributor.institution | University of Aberdeen.Machine Learning | en |
| dc.date.accessioned | 2021-05-29T23:14:39Z | |
| dc.date.available | 2021-05-29T23:14:39Z | |
| dc.date.embargoedUntil | 2021-05-30 | |
| dc.date.issued | 2020-09 | |
| dc.description | This 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.description.status | Peer reviewed | en |
| dc.format.extent | 12 | |
| dc.format.extent | 1292171 | |
| dc.identifier | 170482700 | |
| dc.identifier | 3562c87e-d3f3-4fef-addb-3b7e2ef9a20f | |
| dc.identifier | 85085739845 | |
| dc.identifier.citation | Chivers, 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.2020.125126 | en |
| dc.identifier.doi | 10.1016/j.jhydrol.2020.125126 | |
| dc.identifier.issn | 0022-1694 | |
| dc.identifier.other | ORCID: /0000-0001-6671-5568/work/76211663 | |
| dc.identifier.uri | https://hdl.handle.net/2164/16578 | |
| dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85085739845&partnerID=8YFLogxK | en |
| dc.identifier.vol | 588 | en |
| dc.language.iso | eng | |
| dc.relation.ispartof | Journal of Hydrology | en |
| dc.subject | Machine learning | en |
| dc.subject | Data imputation | en |
| dc.subject | Environmental sensor networks | en |
| dc.subject | Precipitation | en |
| dc.subject | Soil moisture | en |
| dc.subject | Gradient boosted trees | en |
| dc.subject | QA75 Electronic computers. Computer science | en |
| dc.subject | Environmental Science (miscellaneous) | en |
| dc.subject | Artificial Intelligence | en |
| dc.subject | Computer Science Applications | en |
| dc.subject | Water Science and Technology | en |
| dc.subject | Natural Environment Research Council (NERC) | en |
| dc.subject | NE/S016236/1 | en |
| dc.subject | NE/S016244/1 | en |
| dc.subject.lcc | QA75 | en |
| dc.title | Imputation of missing sub-hourly precipitation data in a large sensor network : a machine learning approach | en |
| dc.type | Journal article | en |
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