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Improved Entropy Weighting Model in Water Quality Evaluation

Author

Listed:
  • Yan Feng

    (Nanchang University
    Nanchang University)

  • Yi Fanghui

    (Wuhan University)

  • Chen Li

    (Wuhan University)

Abstract

Entropy weighting model (EWM) is a widely used weighting method in water quality assessment. EWM assigns weights on the basis of the dipartite degree principle. A large weight is assigned for a pollutant with high dipartite degree, and vice versa. However, this dipartite degree principle cannot properly represent the pollutant’s importance through frequent practice when its observation data focus on the worst category. Therefore, weight parameters become illogical. In this study, we reveal this problem through a typical example generated via Monte Carlo simulation. Then, the conventional EWM is improved on the basis of relative entropy theory. In comparison with the conventional EWM, the improved EWM can comprehensively represent indicators’ dipartite degrees and pollution conditions, thereby increasing the rationality of weight results.

Suggested Citation

  • Yan Feng & Yi Fanghui & Chen Li, 2019. "Improved Entropy Weighting Model in Water Quality Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2049-2056, April.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:6:d:10.1007_s11269-019-02227-6
    DOI: 10.1007/s11269-019-02227-6
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    References listed on IDEAS

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    1. Yuankun Wang & Dong Sheng & Dong Wang & Huiqun Ma & Jichun Wu & Feng Xu, 2014. "Variable Fuzzy Set Theory to Assess Water Quality of the Meiliang Bay in Taihu Lake Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 867-880, February.
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    Cited by:

    1. Mengdie Zhao & Jinhang Li & Jinliang Zhang & Yuping Han & Runxiang Cao, 2022. "Research on Evaluation Method for Urban Water Circulation Health and Related Applications: A Case Study of Zhengzhou City, Henan Province," IJERPH, MDPI, vol. 19(17), pages 1-15, August.
    2. Qian Bao & Zhu Yuxin & Wang Yuxiao & Yan Feng, 2020. "Can Entropy Weight Method Correctly Reflect the Distinction of Water Quality Indices?," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(11), pages 3667-3674, September.
    3. Tunis, Sean & Hanna, Eve & Neumann, Peter J. & Toumi, Mondher & Dabbous, Omar & Drummond, Michael & Fricke, Frank-Ulrich & Sullivan, Sean D. & Malone, Daniel C. & Persson, Ulf & Chambers, James D., 2021. "Variation in market access decisions for cell and gene therapies across the United States, Canada, and Europe," Health Policy, Elsevier, vol. 125(12), pages 1550-1556.
    4. Chinanu O. Unigwe & Johnbosco C. Egbueri, 2023. "Drinking water quality assessment based on statistical analysis and three water quality indices (MWQI, IWQI and EWQI): a case study," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(1), pages 686-707, January.
    5. Jingjing Xia & Jin Zeng, 2022. "Environmental Factors Assisted the Evaluation of Entropy Water Quality Indices with Efficient Machine Learning Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2045-2060, April.
    6. Zida Song & Quan Liu & Zhigen Hu, 2020. "Decision-Making Framework, Enhanced by Mutual Inspection for First-Stage Dam Construction Diversion Scheme Selection," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 563-577, January.

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