Imputation of missing sub-hourly precipitation data in a large sensor network : a machine learning approach

Chivers, BenedictWallbank, JohnCole, StevenSebek, OndrejStanley, SimonFry, MatthewLeontidis, Georgios2021-05-292021-05-292020-09Chivers, 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.2020.1251260022-1694ORCID: /0000-0001-6671-5568/work/76211663https://hdl.handle.net/2164/16578This 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].121292171engMachine learningData imputationEnvironmental sensor networksPrecipitationSoil moistureGradient boosted treesQA75 Electronic computers. Computer scienceEnvironmental Science (miscellaneous)Artificial IntelligenceComputer Science ApplicationsWater Science and TechnologyNatural Environment Research Council (NERC)NE/S016236/1NE/S016244/1QA75Imputation of missing sub-hourly precipitation data in a large sensor network : a machine learning approachJournal article10.1016/j.jhydrol.2020.125126http://www.scopus.com/inward/record.url?scp=85085739845&partnerID=8YFLogxK588