Kalman filtering assimilated machine learning methods significantly improve the prediction performance of water quality parameters

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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Bibliographic Details
Main Authors: Zhenyu Gao, Guoqiang Wang, Jinyue Chen, Lei Fang, Shilong Ren, A. Yinglan, Shuping Ji, Ruobing Liu, Qiao Wang
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:Ecological Informatics
Subjects:
Kalman filter
Time series prediction
Machine learning
Data assimilation
Online Access:http://www.sciencedirect.com/science/article/pii/S1574954125003462
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