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dc.date.accessioned2020-09-11T15:24:19Z
dc.date.available2020-09-11T15:24:19Z
dc.date.issued2020-07-28
dc.identifierdoi:10.17170/kobra-202009111783
dc.identifier.urihttp://hdl.handle.net/123456789/11808
dc.description.sponsorshipGefördert durch den Publikationsfonds der Universität Kasselger
dc.language.isoengeng
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectflood event separationeng
dc.subjectinformation extractioneng
dc.subjecttime serieseng
dc.subjectautomationeng
dc.subjectdata preprocessingeng
dc.subject.ddc004
dc.subject.ddc550
dc.titleOn the Automation of Flood Event Separation From Continuous Time Serieseng
dc.typeAufsatz
dcterms.abstractCan machine learning effectively lower the effort necessary to extract important information from raw data for hydrological research questions? On the example of a typical water-management task, the extraction of direct runoff flood events from continuous hydrographs, we demonstrate how machine learning can be used to automate the application of expert knowledge to big data sets and extract the relevant information. In particular, we tested seven different algorithms to detect event beginning and end solely from a given excerpt from the continuous hydrograph. First, the number of required data points within the excerpts as well as the amount of training data has been determined. In a local application, we were able to show that all applied Machine learning algorithms were capable to reproduce manually defined event boundaries. Automatically delineated events were afflicted with a relative duration error of 20 and 5% event volume. Moreover, we could show that hydrograph separation patterns could easily be learned by the algorithms and are regionally and trans-regionally transferable without significant performance loss. Hence, the training data sets can be very small and trained algorithms can be applied to new catchments lacking training data. The results showed the great potential of machine learning to extract relevant information efficiently and, hence, lower the effort for data preprocessing for water management studies. Moreover, the transferability of trained algorithms to other catchments is a clear advantage to common methods.eng
dcterms.accessRightsopen access
dcterms.creatorOppel, Henning
dcterms.creatorMewes, Benjamin
dc.relation.doidoi:10.3389/frwa.2020.00018
dc.subject.swdHochwasserger
dc.subject.swdHydrologieger
dc.subject.swdForschungsdatenger
dc.subject.swdMaschinelles Lernenger
dc.subject.swdInformation Extractionger
dc.subject.swdZeitreiheger
dc.subject.swdAutomationger
dc.subject.swdDatenverarbeitungger
dc.type.versionpublishedVersion
dcterms.source.identifierEISSN: 2624-9375
dcterms.source.journalFrontiers in Watereng
dcterms.source.pageinfoArticle 18
dcterms.source.volumeVolume 2
kup.iskupfalse


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Namensnennung 4.0 International
Except where otherwise noted, this item's license is described as Namensnennung 4.0 International