Kalman filtering assimilated machine learning methods significantly improve the prediction performance of water quality parameters
Accurate water quality prediction is essential for effective water pollution prevention and emergency responses. However, existing research on machine learning (ML)-based data assimilation methods remains limited, particularly in terms of addressing the combined impacts of climate change and anthrop...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-12-01
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| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125003462 |
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