Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China-Reference-Cited by-同舟云学术

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Water quality prediction based on recurrent neural network and improved evidence theory: a case study of Qiantang River, China

Published:2019-05-15 Issue:19 Volume:26 Page:19879-19896
ISSN:0944-1344
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Li Lei,Jiang Peng,Xu Huan,Lin Guang,Guo Dong,Wu Hui

Funder

International Science and Technology Cooperation Program of Zhejiang Province for Joint Research in High-tech Industry

National Key R&D Program of China

Provincial Key R&D Program of Zhejiang Province

National Natural Science Foundation of China and Zhejiang Joint Fund for Integrating of Informatization and Industrialization

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/s11356-019-05116-y.pdf

Reference58 articles.

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2. Alizadeh MJ, Nodoushan EJ, Kalarestaghi N et al (2017) Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models. Environ Sci Pollut Res 24(36):28017–28025

3. Antanasijević D, Pocajt V, Povrenović D, Perić-Grujić A, Ristić M (2013) Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study. Environ Sci Pollut Res 20(12):9006–9013

4. Arya FK, Zhang L (2015) Time series analysis of water quality parameters at Stillaguamish River using order series method. Stoch Env Res Risk A 29(1):227–239

5. Barzegar R, Adamowski J, Moghaddam AA (2016) Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran. Stoch Env Res Risk A 30(7):1797–1819

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