Towards Global Estimation of Riverine Suspended Sediment Flux Using Deep Learning
Riverine suspended sediment flux (SSF) is an important measure of water quality; however, limited in situ observations hinder our ability to comprehensively assess SSF at large scales. This study employed a deep learning approach (i.e., the Long Short
Authors
- Luísa Vieira Lucchese (ORCID: https://orcid.org/0000-0002-8737-7171)
- John Gardner (ORCID: https://orcid.org/0000-0002-1454-5074)
- Abhinav Gupta (ORCID: https://orcid.org/0000-0003-1513-141X)
- Dongmei Feng (ORCID: https://orcid.org/0000-0003-3141-0371)
- Punwath Prum (ORCID: https://orcid.org/0009-0009-7085-7695)
- Praval Devkota (ORCID: https://orcid.org/0000-0002-9303-0940)
Institutions
- University of Pittsburgh (US)
- University of Cincinnati (US)
Publication Details
- Published
- 2025-09-29
- DOI
- https://doi.org/10.22541/essoar.175915616.60776552/v1
- Primary Topic
- Hydrology and Sediment Transport Processes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Aeronautics and Space Administration
- University of Cincinnati
- Nuclear Safety and Security Commission