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An Evidential Reasoning Method of Comprehensive Evaluation of Water Quality Based on Gaussian Distribution

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  • Yangyan Zeng

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China
    Xiangjiang Laboratory, Changsha 410205, China)

  • Xiangzhi Zhang

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China)

  • Wenzhi Cao

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China
    Xiangjiang Laboratory, Changsha 410205, China)

  • Jilin Deng

    (School of Frontier Crossover Studies, Hunan University of Technology and Business, Changsha 410205, China)

  • Hao Zeng

    (China Rongtong Group Information Technology Co., Ltd., Beijing 100089, China)

Abstract

This study provides an evidential reasoning method for water quality evaluation based on Gaussian distribution to handle the problem of comprehensive water quality evaluation for a region across a period (multiple sections and multiple time points). The method turns the collection of observed water quality indicator values into a probability distribution of water quality grades by using the Gaussian distribution to compute the confidence assessment of water quality grades over one period. It eliminates the subjectivity involved in determining confidence levels and the problem of information loss during data fusion that arises with conventional approaches. The probability distribution of each assessment grade is then determined by repeatedly synthesizing evidence of the same water quality grade using the improved evidential reasoning synthesis rule. To avoid the subjectivity included in experience-based weight settings, principal component analysis (PCA) is utilized to calculate the weights of water quality indicators based on contribution rates and load coefficients. In the end, utility theory is presented to modify the discrete probability distribution of precise numerical expressions, offering thorough results for the evaluation of water quality and facilitating the comparison of various water quality grades. Using the Xiangjiang River Basin as a case study, the proposed evaluation method is contrasted with popular techniques for assessing water quality, including the Single-Factor Evaluation Method, the Fuzzy Comprehensive Evaluation Method, and the Evidential Reasoning Comprehensive Evaluation Method. The findings suggest that the evidence reasoning approach for evaluating water quality that is based on Gaussian distribution is more rational, accurate, and scientific. Additionally, empirical studies on the annual water quality trends in various regions, the upstream, midstream, and downstream trends, and the water quality trends during wet and dry periods are conducted using this method to assess and analyze changes in water quality in the Xiangjiang River Basin during the “11th Five-Year Plan” and “12th Five-Year Plan” periods. The analysis findings demonstrate that, even if the rate of progress has slowed, the Xiangjiang River Basin’s overall water quality has been steadily improving since management and protection measures were put in place. This shows that the preventive and control efforts implemented in the “11th Five-Year Plan” and “12th Five-Year Plan” periods were successful; nevertheless, carrying out the current tactics might only have a limited impact. As a result, more advanced and creative approaches are required to encourage the ongoing enhancement of the water quality in the Xiangjiang River Basin.

Suggested Citation

  • Yangyan Zeng & Xiangzhi Zhang & Wenzhi Cao & Jilin Deng & Hao Zeng, 2024. "An Evidential Reasoning Method of Comprehensive Evaluation of Water Quality Based on Gaussian Distribution," Sustainability, MDPI, vol. 16(20), pages 1-16, October.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:20:p:9079-:d:1502574
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    References listed on IDEAS

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    1. Srivastava, Rajendra P., 2011. "An introduction to evidential reasoning for decision making under uncertainty: Bayesian and belief function perspectives," International Journal of Accounting Information Systems, Elsevier, vol. 12(2), pages 126-135.
    2. Xu, Dong-Ling & Yang, Jian-Bo & Wang, Ying-Ming, 2006. "The evidential reasoning approach for multi-attribute decision analysis under interval uncertainty," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1914-1943, November.
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