Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring-Reference-Cited by-同舟云学术

Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring

Published:2013-08-16 Issue:3 Volume:21 Page:1658-1670
ISSN:0944-1344
Container-title:Environmental Science and Pollution Research
language:en
Short-container-title:Environ Sci Pollut Res

Author:

Najah A.,El-Shafie A.,Karim O. A.,El-Shafie Amr H.

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/s11356-013-2048-4.pdf

Reference33 articles.

1. Ahmed AN, Elshafie A, Karim O, Jaffar O (2009) Evaluation the efficiency of radial basis function neural network for prediction of water quality parameters. Eng Intell Syst 17(4):221–231

2. Beale H, Demuth HB (2001) Fuzzy systems toolbox for use with MATLAB, 1st edn. International Thomson Publishing, Waltham

3. Biswas AK (1981) Models for water quality management. McGraw-Hill, New York

4. Chang FJ, Chang YT (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29(1):1–10

5. Das J, Acharya BC (2003) Hydrology and assessment of lotic water quality in Cuttack City, India. J Water Air Soil Pollut 150:163–175

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