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Water Quality Assessment in the Harbin Reach of the Songhuajiang River (China) Based on a Fuzzy Rough Set and an Attribute Recognition Theoretical Model

Author

Listed:
  • Yan An

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Zhihong Zou

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Ranran Li

    (School of Economics and Management, Beihang University, Beijing 100191, China)

Abstract

A large number of parameters are acquired during practical water quality monitoring. If all the parameters are used in water quality assessment, the computational complexity will definitely increase. In order to reduce the input space dimensions, a fuzzy rough set was introduced to perform attribute reduction. Then, an attribute recognition theoretical model and entropy method were combined to assess water quality in the Harbin reach of the Songhuajiang River in China. A dataset consisting of ten parameters was collected from January to October in 2012. Fuzzy rough set was applied to reduce the ten parameters to four parameters: BOD 5 , NH 3 -N, TP, and F. coli (Reduct A). Considering that DO is a usual parameter in water quality assessment, another reduct, including DO, BOD 5 , NH 3 -N, TP, TN, F, and F. coli (Reduct B), was obtained. The assessment results of Reduct B show a good consistency with those of Reduct A, and this means that DO is not always necessary to assess water quality. The results with attribute reduction are not exactly the same as those without attribute reduction, which can be attributed to the α value decided by subjective experience. The assessment results gained by the fuzzy rough set obviously reduce computational complexity, and are acceptable and reliable. The model proposed in this paper enhances the water quality assessment system.

Suggested Citation

  • Yan An & Zhihong Zou & Ranran Li, 2014. "Water Quality Assessment in the Harbin Reach of the Songhuajiang River (China) Based on a Fuzzy Rough Set and an Attribute Recognition Theoretical Model," IJERPH, MDPI, vol. 11(4), pages 1-14, March.
    • Handle: RePEc:gam:jijerp:v:11:y:2014:i:4:p:3507-3520:d:34453
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    Citations

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    Cited by:

    1. Yucheng Liu & Chuansheng Wang & Yutong Chun & Luxin Yang & Wei Chen & Jack Ding, 2019. "A Novel Method in Surface Water Quality Assessment Based on Improved Variable Fuzzy Set Pair Analysis," IJERPH, MDPI, vol. 16(22), pages 1-24, November.
    2. Kunyang Li & Song Cui & Fuxiang Zhang & Rupert Hough & Qiang Fu & Zulin Zhang & Shang Gao & Lihui An, 2020. "Concentrations, Possible Sources and Health Risk of Heavy Metals in Multi-Media Environment of the Songhua River, China," IJERPH, MDPI, vol. 17(5), pages 1-16, March.
    3. Shiguo Xu & Tianxiang Wang & Suduan Hu, 2015. "Dynamic Assessment of Water Quality Based on a Variable Fuzzy Pattern Recognition Model," IJERPH, MDPI, vol. 12(2), pages 1-19, February.

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