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Predict water quality using an improved deep learning method based on spatiotemporal feature correlated: a case study of the Tanghe Reservoir in China

Crossref DOI link: https://doi.org/10.1007/s00477-023-02405-4

Published Online: 2023-03-23

Published Print: 2023-07

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Han, Min

Su, Ziyan

Na, Xiaodong
Funding

Funding for this research was provided by:

National Natural Science Foundation of China (62173063)
License Information

Text and Data Mining valid from 2023-03-23

Version of Record valid from 2023-03-23
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Article History

Accepted: 11 February 2023

First Online: 23 March 2023

Declarations

:

: The authors declare no conflict of interest.

: Not applicable.

: Not applicable.

: Not applicable.

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