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dc.contributor.authorMuraoka, Kohji
dc.contributor.authorHanson, Paul
dc.contributor.authorFrank, Eibe
dc.contributor.authorJiang, Meilan
dc.contributor.authorChiu, Kenneth
dc.contributor.authorHamilton, David
dc.date.accessioned2019-06-07T01:44:06Z
dc.date.available2019-06-07T01:44:06Z
dc.date.issued2018
dc.identifier.issn1541-5856
dc.identifier.doi10.1002/lom3.10283
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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherJohn Wiley and Sons
dc.publisher.placeUnited States of America
dc.relation.ispartofpagefrom787
dc.relation.ispartofpageto801
dc.relation.ispartofissue11
dc.relation.ispartofjournalLimnology and Oceanography: Methods
dc.relation.ispartofvolume16
dc.subject.fieldofresearchEarth Sciences not elsewhere classified
dc.subject.fieldofresearchEarth Sciences
dc.subject.fieldofresearchBiological Sciences
dc.subject.fieldofresearchcode049999
dc.subject.fieldofresearchcode04
dc.subject.fieldofresearchcode06
dc.subject.keywordsDissolved oxygen
dc.subject.keywordsSensor observations
dc.subject.keywordsLake metabolism analysis
dc.subject.keywordsSymbolic Aggregate approXimation (SAX)
dc.titleA data mining approach to evaluate suitability of dissolved oxygen sensor observations for lake metabolism analysis
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorHamilton, David P.


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