A novel hybrid model based on two-stage data processing and machine learning for forecasting chlorophyll-a concentration in reservoirs-Reference-Cited by-同舟云学术

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A novel hybrid model based on two-stage data processing and machine learning for forecasting chlorophyll-a concentration in reservoirs

Published:2023-11-28 Issue:1 Volume:31 Page:262-279
ISSN:1614-7499
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Yu Wenqing,Wang Xingju,Jiang Xin,Zhao Ranhang^ORCID,Zhao Shen

Funder

Research Fund of Water Research Institute of Shandong Province

National Natural Science Foundation of China

Shandong Province Water Conservancy Research and Technology Promotion Program

Jinan Water Science and Technology Program

Publisher

Springer Science and Business Media LLC

Subject

Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s11356-023-31148-6.pdf

Reference47 articles.

1. Alexakis D, Kagalou I, Tsakiris G (2013) Assessment of pressures and impacts on surface water bodies of the Mediterranean. Case study: Pamvotis Lake, Greece. Environ Earth Sci 70:687–698. https://doi.org/10.1007/s12665-012-2152-7

2. Antico A, Schlotthauer G, Torres ME (2014) Analysis of hydroclimatic variability and trends using a novel empiricalmode decomposition: application to the Parana River Basin. J Geophys Res-Atmos 119:1218–1233. https://doi.org/10.1002/2013jd020420

3. Boyer JN, Kelble CR, Ortner PB, Rudnick DT (2009) Phytoplankton bloom status: chlorophyll a biomass as an indicator of water quality condition in the southern estuaries of Florida, USA. Ecol Indic 9:S56–S67. https://doi.org/10.1016/j.ecolind.2008.11.013

4. Carvalho L, Miller CA, Scott EM, Codd GA, Davies PS, Tyler AN (2011) Cyanobacterial blooms: statistical models describing risk factors for national-scale lake assessment and lake management. Sci Total Environ 409:5353–5358. https://doi.org/10.1016/j.scitotenv.2011.09.030

5. Cen HB, Jiang JH, Han GQ, Lin XY, Liu Y, Jia XY et al (2022) Applying deep learning in the prediction of chlorophyll-a in the East China Sea. Remote Sens 14:16. https://doi.org/10.3390/rs14215461

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