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dc.contributor.authorMuraoka, Kohjien_US
dc.contributor.authorHanson, Paulen_US
dc.contributor.authorFrank, Eibeen_US
dc.contributor.authorJiang, Meilanen_US
dc.contributor.authorChiu, Kennethen_US
dc.contributor.authorHamilton, Daviden_US
dc.date.accessioned2019-06-07T01:44:06Z
dc.date.available2019-06-07T01:44:06Z
dc.date.issued2018en_US
dc.identifier.issn1541-5856en_US
dc.identifier.doi10.1002/lom3.10283en_US
dc.identifier.urihttp://hdl.handle.net/10072/382157
dc.description.abstractDespite rapid growth in continuous monitoring of dissolved oxygen for lake metabolism studies, the current best practice still relies on visual assessment and manual data filtering of sensor observations by experienced scientists in order to achieve meaningful results. This time consuming approach is fraught with potential for inconsistency and individual subjectivity. An automated method to assure the quality of data for the purpose of metabolism modeling is clearly needed to obtain consistent results representative of collective expertise. We used a hybrid approach of expert panel and data mining for data filtration. Symbolic Aggregate approXimation (SAX) treats discretized numerical timeseries segments as symbolic indications, creating a series of strings which are literally comparable to human words and sentences. This conversion allows established text mining techniques, such as classification methods to be applied to timeseries data. Half‐hourly frequency surface dissolved oxygen data from 18 global lakes were used to create day‐long segments of the original time series data. Three hundred sets of 1‐d measurements were provided to a group of seven anonymous experts, experienced in manual filtering of oxygen data for metabolism modeling studies. The collective results were treated as expert panel decisions, and were used to rank the data by confidence level for use in metabolism calculations. While considerable variation occurred in the way the experts perceived the quality of the data, the model provides an objective and quantitative assessment method. The program output will assist the decision making process in determining whether data should be used for metabolism calculations. An R version of the program is available for download.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherJohn Wiley and Sonsen_US
dc.publisher.placeUnited States of Americaen_US
dc.relation.ispartofpagefrom787en_US
dc.relation.ispartofpageto801en_US
dc.relation.ispartofissue11en_US
dc.relation.ispartofjournalLimnology and Oceanography: Methodsen_US
dc.relation.ispartofvolume16en_US
dc.subject.fieldofresearchEarth Sciences not elsewhere classifieden_US
dc.subject.fieldofresearchEarth Sciencesen_US
dc.subject.fieldofresearchBiological Sciencesen_US
dc.subject.fieldofresearchcode049999en_US
dc.subject.fieldofresearchcode04en_US
dc.subject.fieldofresearchcode06en_US
dc.subject.keywordsDissolved oxygenen_US
dc.subject.keywordsSensor observationsen_US
dc.subject.keywordsLake metabolism analysisen_US
dc.subject.keywordsSymbolic Aggregate approXimation (SAX)en_US
dc.titleA data mining approach to evaluate suitability of dissolved oxygen sensor observations for lake metabolism analysisen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dc.type.codeC - Journal Articlesen_US
gro.hasfulltextNo Full Text


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