The improved entropy weighting model in water quality evaluation based on the compound function.
Compound function
Dispersion degree
Entropy weight model
Pollution degree
Poyang Lake
Water quality evaluation
Journal
Environmental monitoring and assessment
ISSN: 1573-2959
Titre abrégé: Environ Monit Assess
Pays: Netherlands
ID NLM: 8508350
Informations de publication
Date de publication:
10 Aug 2022
10 Aug 2022
Historique:
received:
14
02
2022
accepted:
12
07
2022
entrez:
10
8
2022
pubmed:
11
8
2022
medline:
13
8
2022
Statut:
epublish
Résumé
Entropy weight model (EWM) is widely used in water quality evaluation. In the conventional EWM, the weight is a monotone increasing function of the dispersion degree. However, this weighting principle often neglects the heavily polluted indicators. To solve this problem, an improved EWM is designed, in which the weight of the indicator is a compound function of its dispersion degree and pollution degree. In the clean domain, the weight increases with the dispersion degree, while in the polluted domain, the weight decreases with the dispersion degree. And for the same dispersion degree, the larger the pollution degree is, the higher the weight is, and vice versa. Subsequently, the improved EWM is applied to the water quality evaluation of Wucheng Wetland in Poyang Lake, China. Results are as follows: (i) For TP, COD
Identifiants
pubmed: 35947232
doi: 10.1007/s10661-022-10304-4
pii: 10.1007/s10661-022-10304-4
doi:
Substances chimiques
Phosphorus
27YLU75U4W
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
662Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Références
Avvannavar, S. M., & Shrihari, S. (2007). Determination of water quality deterioration at pilgrimage centre along river Netravathi, Mangalore using WQI approach. Environmental Engineering and Management Journal, 6(2), 123–131.
doi: 10.30638/eemj.2007.017
Agency, C. E. P. (2002). National surface water environmental quality standards of China (GB-3838-2002). China Standards Press.
Dai, X. Z., & Hu, Z. P. (2019). Study on resources and environment of Poyang Lake. Science Press.
Finney, W., & Giordano,. (2004). Thomas’ calculus. Higher Education Press.
Fang, N., You, Q. H., Liu, L. L., Li, J. Y., Lu, C. F., Zhang, L., Yang, T., Yu, Z. P., Lǖ, Z. L., & Yang, W. J. (2019). Evaluation of eutrophication in Poyang Lake wetland during autumn based on cloud model. Acta Ecologica Sinica, 39(17), 6314–6321.
Hu, Z. P. (2020). Hydrological and ecological characteristics and evolution of Poyang Lake. Science Press.
Huang, X., Guan, Z. Y., Wang, Q., & Qiu, L. (2012). Application of refined set pair analysis to comprehensive evaluation of lake water quality. Water Resource and Hydropower Engineering, 43(1), 35–37.
Kannel, P. R., Lee, S., Lee, Y. S., Kanel, S. R., & Khan, S. P. (2007). Application of water quality indices and dissolved oxygen as indicators for river water classification and urban impact assessment. Environmental Monitoring and Assessment, 132(1–3), 93–110.
doi: 10.1007/s10661-006-9505-1
Liu, L., Zhou, J. Z., An, X. L., Zhang, Y. C., & Yang, L. (2010). Using fuzzy theory and information entropy for water quality assessment in Three Gorges region. China. Expert Systems with Applications, 37(3), 2517–2521.
doi: 10.1016/j.eswa.2009.08.004
Liu, T. K., Yu, J. L., Chen, C. L., & Wei, P. S. (2012). Information theoretic perspective on coastal water-quality monitoring and management near an offshore industrial park. Environmental Monitoring and Assessment, 184(8), 4725–4735.
doi: 10.1007/s10661-011-2297-y
Li, R. F., & Zhang, Y. (2011). Analysis of spatial and temporal variation of water quality and its influencing factors in Poyang Lake. Water Resources Protection, 27(6), 9–13.
Mogheir, Y., & Singh, V. P. (2002). Application of information theory to groundwater quality monitoring networks. Water Resources Management, 16(1), 37–49.
doi: 10.1023/A:1015511811686
Qian, B., Zhu, Y. X., Wang, Y. X., & Yan, F. (2020). Can entropy weight method correctly reflect the distinction of water quality indices? Water Resources Management, 34(11), 3667–3674.
doi: 10.1007/s11269-020-02641-1
Sahoo, M. M., Patra, K. C., Swain, J. B., & Khatua, K. K. (2016). Evaluation of water quality with application of Bayes’s rule and entropy weight method. European Journal of Environmental and Civil Engineering, 21(6), 1–23.
Singh, K. R., Dutta, R., Kalamdhad, A. S., & Kumar, B. (2019). Information entropy as a tool in surface water quality assessment. Environmental Earth Sciences, 78(1), 1–12.
doi: 10.1007/s12665-018-7998-x
Shannon, C. E. (1948). A mathematical theory of communication. Bell Systems Technical Journal, 27(4), 623–656.
doi: 10.1002/j.1538-7305.1948.tb00917.x
Taheriyoun, M., Karamouz, M., & Baghvand, A. (2010). Development of an entropy- based fuzzy eutrophication index for reservoir water quality evaluation. Iranian Journal of Environmental Health Science and Engineering, 7(1), 1–14.
Vraneevi, M., Beli, S., Kolakovi, S., Kadovic, R., & Bezdan, A. (2017). Estimating suitability of localities for biotechnical measures on drainage system application in Vojvodina. Irrigation and Drainage, 66(1), 129–140.
doi: 10.1002/ird.2024
Wang, S., Huang, J., Feng, S. L., Wan, Z. H., & Wang, Q. (2019). Application of CCME WQI in water quality assessment of Seagoing rivers (the Aojiang River) in China. Environmental Monitoring in China, 35(4), 93–99.
Wang, Y. K., Sheng, D., Wang, D., Ma, H. Q., Wu, J. C., & Xu, F. (2014). Variable fuzzy set theory to assess water quality of the Meiliang Bay in Taihu Lake Basin. Water Resources Management, 28(3), 867–880.
doi: 10.1007/s11269-014-0521-6
Wang, Y. M., Wu, Y. F., & Jiang, L. (2020). Application of interval information comprehensive ranking model based on entropy weight in river water quality evaluation. Journal of Coastal Research, 105, 137–140.
doi: 10.2112/SI99-020.1
Wang, Z., Xing, X. G., Yan, F. (2021). An abnormal phenomenon in entropy weight method in the dynamic evaluation of water quality index. Ecol Indicators, 131.
Yang, G. H., & Cui, B. (2011). The application of entropy weight method to evaluation of the sustainable utilization of water resources. Mathematics in Practice and Theory, 41(19), 8–12.
Yang, J. Y., & Zhang, L. L. (2012). Fuzzy comprehensive evaluation method on water environmental quality based on entropy weight with consideration of toxicology of evaluation factors. Advanced Materials Research, 356–360(1–5), 2383–2388.
Yi, F. H., Chen, Li., & Yan, F. (2019). The health risk weightin g model in groundwater quality evaluation. Human and Ecological Risk Assessment, 25(8), 2089–2097.
doi: 10.1080/10807039.2018.1488581
Yan, F., Liu, L., Li, Y. F., Zhang, Y., Chen, M. S., & Xing, X. G. (2015). A dynamic water quality index model based on functional data analysis. Ecological Indicators, 57, 249–258.
doi: 10.1016/j.ecolind.2015.05.005
Zhang, X. Q., Feng, W. H., & Li, N. N. (2010). Attribute recognition model based on entropy weight and its application to evaluation of groundwater quality. Applied Mechanics and Materials, 29–32, 2698–2702.
doi: 10.4028/www.scientific.net/AMM.29-32.2698
Zhang, X. Q., Liang, C., & Liu, H. Q. (2007). Application of improved TOPSIS method based on coefficient of entropy to comprehensive evaluating water quality. Journal of Harbin Institute of Technology, 39(10), 1670–1672.
Zhu L. Z., Xing, X. G., Yan, F., Lopes A. M. (2021).The abnormal phenomena of entropy weighting method in the dynamic evaluation of agricultural water conservation. Mathematical Problems in Engineering.
Zhu, Y. X., Tian, D. Z., Yan, F., (2020). Effectiveness of entropy weight method in decision making. Mathematical Problems in Engineering, 1–5.
Zou, Z. H., Sun, J. N., & Ren, G. P. (2005). Study and application on the entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment. Acta Scientiae Circumstantiae, 25(4), 552–556.