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Lake water-level fluctuation forecasting using machine learning models: a systematic review.
Environ Sci Pollut Res Int. 2020 Dec;27(36):44807-44819. doi: 10.1007/s11356-020-10917-7. Epub 2020 Sep 25.
Environ Sci Pollut Res Int. 2020.
PMID: 32978734
Review.
Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes.
Luo W, Zhu S, Wu S, Dai J.
Luo W, et al.
Environ Sci Pollut Res Int. 2019 Oct;26(29):30524-30532. doi: 10.1007/s11356-019-06360-y. Epub 2019 Sep 3.
Environ Sci Pollut Res Int. 2019.
PMID: 31482526
Chlorophyll-a (CHLA) is a key indicator to represent eutrophication status in lakes. In this study, CHLA, total phosphorus (TP), total nitrogen (TN), turbidity (TB), and Secchi depth (SD) collected by the United States Environmental Protection Agency for the National …
Chlorophyll-a (CHLA) is a key indicator to represent eutrophication status in lakes. In this study, CHLA, total phosphorus (TP), tota …
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Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models.
Zhu S, Heddam S, Nyarko EK, Hadzima-Nyarko M, Piccolroaz S, Wu S.
Zhu S, et al.
Environ Sci Pollut Res Int. 2019 Jan;26(1):402-420. doi: 10.1007/s11356-018-3650-2. Epub 2018 Nov 7.
Environ Sci Pollut Res Int. 2019.
PMID: 30406582
River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. ...
River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on …
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