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LEE, T. LEE, J. PARK, J. BELLEROVÁ, H. RAUDENSKÝ, M.
Originální název
Reconstructive Mapping from Sparsely-Sampled Groundwater Data Using Compressive Sensing
Typ
článek v časopise - ostatní, Jost
Jazyk
angličtina
Originální abstrakt
Compressive 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.
Klíčová slova
Visualization Data, Compressive Sensing, Reconstruction, Mapping
Autoři
LEE, T.; LEE, J.; PARK, J.; BELLEROVÁ, H.; RAUDENSKÝ, M.
Vydáno
10. 5. 2021
Nakladatel
Scientific Research Publishing
ISSN
0269-3798
Periodikum
International Journal of Geographical Information Systems
Ročník
13
Číslo
3
Stát
Spojené království Velké Británie a Severního Irska
Strany od
287
Strany do
301
Strany počet
15
URL
https://www.scirp.org/journal/paperinformation.aspx?paperid=108983
Plný text v Digitální knihovně
http://hdl.handle.net/11012/203373
BibTex
@article{BUT171469, author="Taewoo {Lee} and Joon Young {Lee} and Jung Eun {Park} and Hana {Bellerová} and Miroslav {Raudenský}", title="Reconstructive Mapping from Sparsely-Sampled Groundwater Data Using Compressive Sensing", journal="International Journal of Geographical Information Systems", year="2021", volume="13", number="3", pages="287--301", doi="10.4236/jgis.2021.133016", issn="0269-3798", url="https://www.scirp.org/journal/paperinformation.aspx?paperid=108983" }