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
Abstract
This study aims to improve the accuracy of bathymetry predicted by gravity-geologic method (GGM) using the optimal machine learning model selected from machine learning techniques. In this study, several machine learning techniques were utilized to determine the optimal model from the performance of depth and gravity anomalies. In addition, a tuning density contrast calculated from satellite altimetry-derived free-air gravity anomalies (FAGAs) was applied to estimate enhanced bathymetry. By comparison with shipborne depth, the accuracy of the bathymetry estimated by using satellite altimetry-derived FAGAs and machine learning was evaluated. The findings reveal that the bathymetry predicted by the optimal machine learning using the Gaussian process regression and the GGM with a tuning density contrast can enhance the accuracy of 82.64 m, showing an improvement of 67.40% in the RMSE at shipborne depth measurements. Although the tuning density is larger than 1.67 g/cm3, bathymetry using satellite altimetry-derived FAGAs and machine learning can be effectively improved with higher accuracy.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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: |
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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): | oai:eprints.whiterose.ac.uk:216684 |