Reconstructive Mapping from Sparsely-Sampled Groundwater Data Using Compressive Sensing

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dc.contributor.authorLee, Taewoocs
dc.contributor.authorLee, Joon Youngcs
dc.contributor.authorPark, Jung Euncs
dc.contributor.authorBellerová, Hanacs
dc.contributor.authorRaudenský, Miroslavcs
dc.coverage.issue3cs
dc.coverage.volume13cs
dc.date.accessioned2022-01-24T15:57:22Z
dc.date.available2022-01-24T15:57:22Z
dc.date.issued2021-05-10cs
dc.description.abstractCompressive sensing is a powerful method for reconstruction of sparsely-sampled data, based on statistical optimization. It can be applied to a range of flow measurement and visualization data, and in this work we show the usage in groundwater mapping. Due to scarcity of water in many regions of the world, including southwestern United States, monitoring and management of groundwater is of utmost importance. A complete mapping of groundwater is difficult since the monitored sites are far from one another, and thus the data sets are considered extremely “sparse”. To overcome this difficulty in complete mapping of groundwater, compressive sensing is an ideal tool, as it bypasses the classical Nyquist criterion. We show that compressive sensing can effectively be used for reconstructions of groundwater level maps, by validating against data. This approach can have an impact on geographical sensing and information, as effective monitoring and management are enabled without constructing numerous or expensive measurement sites for groundwater.en
dc.formattextcs
dc.format.extent287-301cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationInternational Journal of Geographical Information Systems. 2021, vol. 13, issue 3, p. 287-301.en
dc.identifier.doi10.4236/jgis.2021.133016cs
dc.identifier.issn0269-3798cs
dc.identifier.other171469cs
dc.identifier.urihttp://hdl.handle.net/11012/203373
dc.language.isoencs
dc.publisherScientific Research Publishingcs
dc.relation.ispartofInternational Journal of Geographical Information Systemscs
dc.relation.urihttps://www.scirp.org/journal/paperinformation.aspx?paperid=108983cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/0269-3798/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectVisualization Dataen
dc.subjectCompressive Sensingen
dc.subjectReconstructionen
dc.subjectMappingen
dc.titleReconstructive Mapping from Sparsely-Sampled Groundwater Data Using Compressive Sensingen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-171469en
sync.item.dbtypeVAVen
sync.item.insts2022.02.03 12:53:19en
sync.item.modts2022.02.03 12:14:07en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Laboratoř přenosu tepla a prouděnícs
thesis.grantorVysoké učení technické v Brně. . Arizona State Universitycs
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