Detail publikace

Reconstructive Mapping from Sparsely-Sampled Groundwater Data Using Compressive Sensing

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"
}