Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams
•We developed a deep learning-based framework for long-term Chl-a simulation.•The performance of six state of the art deep learning algorithms was compared.•Our study employed separate sub-basins to train and evaluate DL models.•Chl-a prediction is improved by using sub-basin characteristics as inpu...
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| Published in: | Journal of hydrology (Amsterdam) Vol. 626; p. 130240 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.11.2023
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| Subjects: | |
| ISSN: | 0022-1694, 1879-2707 |
| Online Access: | Get full text |
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