Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea

Kim, K.B., Kim, J. orcid.org/0000-0003-2280-036X and Yun, H.S. (2024) Improved Bathymetry Estimation Using Satellite Altimetry-Derived Gravity Anomalies and Machine Learning in the East Sea. Journal of Marine Science and Engineering, 12 (9). 1520. ISSN 2077-1312

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Item Type: Article
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Keywords: optimal machine learning; gravity anomalies; density contrast; east sea
Dates:
  • Published: September 2024
  • Published (online): 2 September 2024
  • Accepted: 1 September 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 05 Sep 2024 10:26
Last Modified: 05 Sep 2024 10:26
Status: Published
Publisher: MDPI
Identification Number: 10.3390/jmse12091520
Open Archives Initiative ID (OAI ID):

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