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Public Satisfaction with Water Quality Under the Implementation of Water Quality Monitor Standard System

Author

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  • Wenwen Wu

    (Hebei University of Engineering)

  • Zilin Wei

    (Hebei University of Engineering)

  • Lifeng Wu

    (Hebei University of Engineering)

Abstract

With the continuous development of China’s social economy and residents’living standards, the public’s requirements for water quality continue to improve. And China’s water quality monitoring standard system is also gradually improving and developing. The prediction of public water quality satisfaction has a significant impact on the subsequent improvement of the water quality monitoring standard system in China. Compared with previous studies, the novelty of this paper lies in the use of grey multivariate model to study public water quality satisfaction. The grey multivariate model FGMC(1, N) is used to predict public water quality satisfaction in seven representative provinces in Eastern, central, northern, northwestern and southwestern China. The results show that public satisfaction with water quality is on the rise in Fujian, Hebei and Sichuan, while it is on the decline in Jiangsu, Henan, Xinjiang and Shaanxi. The results of this study will provide an important reference for the future improvment of China’s water quality monitoring standard system.

Suggested Citation

  • Wenwen Wu & Zilin Wei & Lifeng Wu, 2024. "Public Satisfaction with Water Quality Under the Implementation of Water Quality Monitor Standard System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4197-4212, September.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:11:d:10.1007_s11269-024-03859-z
    DOI: 10.1007/s11269-024-03859-z
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    References listed on IDEAS

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    1. Jiafeng Pang & Wei Luo & Zeyu Yao & Jing Chen & Chunyu Dong & Kairong Lin, 2024. "Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(7), pages 2399-2420, May.
    2. Yijue Sun & Fenglin Zhang, 2022. "Grey Multivariable Prediction Model of Energy Consumption with Different Fractional Orders," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    3. Qingqing Zhang & Xue-yi You, 2024. "Recent Advances in Surface Water Quality Prediction Using Artificial Intelligence Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 235-250, January.
    4. Wang, Siwei & Xiao, Xinping & Ding, Qi, 2024. "A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery," Energy, Elsevier, vol. 290(C).
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    Cited by:

    1. Germán Santacruz León & José Alfredo Ramos-Leal & Janete Morán Ramírez & Oscar Guadalupe Almanza-Tovar, 2025. "Drought and Water Quality in a Semi-arid Area: Effects in Livestock Production, Agriculture and Use Urban," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(4), pages 1605-1621, March.

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