IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i20p9079-d1502574.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/20/9079/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/20/9079/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gao, Bin & Ni, Ming-Fang, 2009. "A note on article "The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees"," European Journal of Operational Research, Elsevier, vol. 197(2), pages 809-812, September.
    2. Adi Masli & Matthew G. Sherwood & Rajendra P. Srivastava, 2018. "Attributes and Structure of an Effective Board of Directors: A Theoretical Investigation," Abacus, Accounting Foundation, University of Sydney, vol. 54(4), pages 485-523, December.
    3. Ni, Lei & Chen, Yu-wang & de Brujin, Oscar, 2021. "Towards understanding socially influenced vaccination decision making: An integrated model of multiple criteria belief modelling and social network analysis," European Journal of Operational Research, Elsevier, vol. 293(1), pages 276-289.
    4. J-B Yang & D-L Xu & X Xie & A K Maddulapalli, 2011. "Multicriteria evidential reasoning decision modelling and analysis—prioritizing voices of customer," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1638-1654, September.
    5. Fu, Chao & Yang, Shanlin, 2011. "An attribute weight based feedback model for multiple attributive group decision analysis problems with group consensus requirements in evidential reasoning context," European Journal of Operational Research, Elsevier, vol. 212(1), pages 179-189, July.
    6. Fu, Chao & Yang, Shanlin, 2012. "An evidential reasoning based consensus model for multiple attribute group decision analysis problems with interval-valued group consensus requirements," European Journal of Operational Research, Elsevier, vol. 223(1), pages 167-176.
    7. Lianmeng Jiao & Quan Pan & Yan Liang & Xiaoxue Feng & Feng Yang, 2016. "Combining sources of evidence with reliability and importance for decision making," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 24(1), pages 87-106, March.
    8. Yang, Guo-liang & Yang, Jian-bo & Liu, Wen-bin & Li, Xiao-xuan, 2013. "Cross-efficiency aggregation in DEA models using the evidential-reasoning approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 393-404.
    9. Ran Fang & Huchang Liao, 2021. "Emergency material reserve location selection by a time-series-based evidential reasoning approach under bounded rationality," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(4), pages 1397-1417, August.
    10. Zhang, Daihui & Qu, Zhuohau & Wang, Wenxin & Yu, Jiagen & Yang, Zaili, 2020. "New uncertainty modelling for cargo stowage plans of general cargo ships," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    11. Desai, Vikram & Bucaro, Anthony C. & Kim, Joung W. & Srivastava, Rajendra & Desai, Renu, 2023. "Toward a better expert system for auditor going concern opinions using Bayesian network inflation factors," International Journal of Accounting Information Systems, Elsevier, vol. 49(C).
    12. Cui, Huizi & Zhou, Lingge & Li, Yan & Kang, Bingyi, 2022. "Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    13. Fu, Chao & Yang, Shan-Lin, 2010. "The group consensus based evidential reasoning approach for multiple attributive group decision analysis," European Journal of Operational Research, Elsevier, vol. 206(3), pages 601-608, November.
    14. Merigó, José M. & Casanovas, Montserrat & Yang, Jian-Bo, 2014. "Group decision making with expertons and uncertain generalized probabilistic weighted aggregation operators," European Journal of Operational Research, Elsevier, vol. 235(1), pages 215-224.
    15. Beynon, Malcolm J. & Andrews, Rhys & Boyne, George A., 2010. "Evidence-based modelling of strategic fit: An introduction to RCaRBS," European Journal of Operational Research, Elsevier, vol. 207(2), pages 886-896, December.
    16. Guo, Min & Yang, Jian-Bo & Chin, Kwai-Sang & Wang, Hongwei, 2007. "Evidential reasoning based preference programming for multiple attribute decision analysis under uncertainty," European Journal of Operational Research, Elsevier, vol. 182(3), pages 1294-1312, November.
    17. Behnam Vahdani & Meghdad Salimi & Seyed Meysam Mousavi, 2017. "A New Compromise Solution Model Based on Dantzig–Wolfe Decomposition for Solving Belief Multi-Objective Nonlinear Programming Problems with Block Angular Structure," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 333-387, March.
    18. Guo, Min & Chen, Yu-wang & Wang, Hongwei & Yang, Jian-Bo & Zhang, Keyong, 2019. "The single-period (newsvendor) problem under interval grade uncertainties," European Journal of Operational Research, Elsevier, vol. 273(1), pages 198-216.
    19. Shi Qiu & Yuansheng Luo & Hongwei Guo, 2021. "Multisource evidence theory‐based fraud risk assessment of China's listed companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1524-1539, December.
    20. S. Nodoust & A. Mirzazadeh & G.-W. Weber, 2020. "An evidential reasoning approach for production modeling with deteriorating and ameliorating items," Operational Research, Springer, vol. 20(1), pages 1-19, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:20:p:9079-:d:1502574. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.