A data-driven model for real-time water quality prediction and early warning by an integration method-Reference-Cited by-同舟云学术

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A data-driven model for real-time water quality prediction and early warning by an integration method

Published:2019-08-22 Issue:29 Volume:26 Page:30374-30385
ISSN:0944-1344
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Jin Tao,Cai Shaobin,Jiang Dexun,Liu Jie^ORCID

Funder

Heilongjiang Natural Science Foundation

the Fundamental Research Funds in Heilongjiang Provincial Universities

Youth Science Fund of Heilongjiang Province of China

the China Postdoctoral Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

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

Link

http://link.springer.com/content/pdf/10.1007/s11356-019-06049-2.pdf

Reference31 articles.

1. Boucher MA, Perreault L, Anctil F (2009) Tools for the assessment of hydrological ensemble forecasts obtained by neural networks. J Hydroinf 11(3–4):297–307

2. Bowden GJ, Dandy GC, Maier HR (2005) Input determination for neural network models in water resources applications. Part 1-background and methodology. J Hydrol 301(1–4):75–92

3. Burchard-Levine A, Liu SM, Vince F, Li MM, Ostfeld A (2014) A hybrid evolutionary data driven model for river water quality early warning. J Environ Manag 143:8–16

4. Ding YR, Cai YJ, Sun PD, Chen B (2014) The use of combined neural networks and genetic algorithms for prediction of river water quality. J Appl Res Technol 12(3):493–499

5. Halliday SJ, Skeffington RA, Wade AJ, Bowes MJ, Gozzard E, Newman JR (2015) High-frequency water quality monitoring in an urban catchment: hydrochemical dynamics, primary production and implications for the water framework directive. Hydrol Process 29:3388–3407

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