Kechnit, D., Tshimanga, R.M., Ammari, A. et al. (5 more authors) (2024) Bathymetry and discharge estimation in large and data-scarce rivers using an entropy-based approach. Hydrological Sciences Journal, 69 (15). pp. 2109-2123. ISSN 0262-6667
Abstract
This study implements an entropy theory-based approach to infer bathymetry for 29 selected cross-sections along a 1740 km reach of the Congo River. A genetic algorithm optimization approach is used based on an analysis of near-surface velocity measurements to generate a random sample of 1000 bathymetry profiles from which the analysis is carried out. The resulting simulated bathymetry shows good agreement compared to the measurements obtained via Accoustic Doppler Current Profiler (ADCP), with a correlation that varies from 0.49 to 0.88. The bathymetry results are subsequently used to estimate the two-dimensional cross-sectional flow velocity distribution and, consequently, to calculate the river discharge. The mean errors observed for flow area, discharge, and mean velocity are found to be 2.7%, 1.3%, and 1%, respectively. This study demonstrates, for the first time, the successful application of an entropy-based approach to estimate bathymetry and discharge in large rivers and has significant implications for remote sensing applications.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 IAHS. This is an author produced version of an article published in Hydrological Sciences Journal. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | bathymetry, discharge, entropy approach, large rivers, near-surface velocity |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Nov 2024 10:07 |
Last Modified: | 04 Dec 2024 14:28 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/02626667.2024.2402933 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:219669 |
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Filename: kechnit et al. (2024b).pdf
