Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 30, Number 3, June 2025
Page(s) 302 - 312
DOI https://doi.org/10.1051/wujns/2025303302
Published online 16 July 2025
  1. Ma J Q, Guo J J, Liu X J. Water quality evaluation model based on principal component analysis and information entropy: Application in Jinshui River[J]. Journal of Resources and Ecology, 2010, 1(3): 249-251, 252(Ch). [Google Scholar]
  2. Mahjouri N, Kerachian R. Revising river water quality monitoring networks using discrete entropy theory: The Jajrood River experience[J]. Environmental Monitoring and Assessment, 2011, 175(1/2/3/4): 291-302. [Google Scholar]
  3. Paul S, Mishra U. Assessment of underground water quality in North Eastern region of India: A case study of Agartala City[J]. International Journal of Environmental Sciences, 2011, 2(2): 850-862. [Google Scholar]
  4. Li P Y, Qian H, Wu J H. Groundwater quality assessment based on improved water quality index in Pengyang County, Ningxia, Northwest China[J]. Journal of Chemistry, 2010, 7(S1): S209-S216. [Google Scholar]
  5. Li P Y, Qian H, Wu J H. Application of set pair analysis method based on entropy weight in groundwater quality assessment—A case study in Dongsheng City, Northwest China[J]. Journal of Chemistry, 2011, 8(2): 851-858. [Google Scholar]
  6. Li P Y, Qian H, Wu J H. Hydrochemical formation mechanisms and quality assessment of groundwater with improved TOPSIS method in Pengyang County Northwest China[J]. Journal of Chemistry, 2011, 8(3): 1164-1173. [Google Scholar]
  7. Li P Y, Wu J H, Qian H. Groundwater quality assessment based on rough sets attribute reduction and TOPSIS method in a semi-arid area, China[J]. Environmental Monitoring and Assessment, 2012, 184(8): 4841-4854. [Google Scholar]
  8. Li P Y, Qian H, Wu J H, et al. Sensitivity analysis of TOPSIS method in water quality assessment: I. Sensitivity to the parameter weights[J]. Environmental Monitoring and Assessment, 2013, 185(3): 2453-2461. [Google Scholar]
  9. Amiri V, Rezaei M, Sohrabi N. Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran[J]. Environmental Earth Sciences, 2014, 72(9): 3479-3490. [Google Scholar]
  10. Singh V P. Entropy Theory and Its Application in Environmental and Water Engineering[M]. Hoboken: Wiley-Blackwell, 2013. [Google Scholar]
  11. Kholoosi M M, Aberi P, Arabzadeh R, et al. Water quality assessment using entropy based water quality index and application of a new clustering approach[C]// 10th International Congress on Civil Engineering. Tabriz: University of Tabriz, 2015. [Google Scholar]
  12. Li Z Y, Yang T, Huang C S, et al. An improved approach for water quality evaluation: TOPSIS-based informative weighting and ranking (TIWR) approach[J]. Ecological Indicators, 2018, 89: 356-364. [Google Scholar]
  13. Zou Z H, Yun Y, Sun J N. Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment[J]. Journal of Environmental Sciences, 2006, 18(5): 1020-1023. [Google Scholar]
  14. Zhu Z G, Zhang L, Wei S Y. A combination model and application for the water quality evaluation[J]. WSEAS Transactions on Systems, 2009, 85: 628-637. [Google Scholar]
  15. Liu L, Zhou J Z, An X L, et al. Using fuzzy theory and information entropy for water quality assessment in Three Gorges region, China[J]. Expert Systems with Applications, 2010, 37(3): 2517-2521. [Google Scholar]
  16. Xing Z X, Fu Q, Liu D. Water quality evaluation by the fuzzy comprehensive evaluation based on EW method[C]//2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). New York: IEEE, 2011: 476-479. [Google Scholar]
  17. Hung C C, Chen L H. A fuzzy TOPSIS decision making model with entropy weight under intuitionistic fuzzy environment [C]// Proceedings of the International Multiconference of Engineers and Computer Scientists. Hong Kong: International Association of Engineers 2009, 1: 13-16. [Google Scholar]
  18. Won K, Chung E S, Choi S U. Parametric assessment of water use vulnerability variations using SWAT and fuzzy TOPSIS coupled with entropy[J]. Sustainability, 2015, 7(9): 12052-12070. [Google Scholar]
  19. Komasi M, Sharghi S. Surface water quality assessment and prioritize the factors pollute this water using TOPSIS fuzzy hierarchical analysis[J]. Journal of Environmental Health Engineering, 2017, 4(2): 174-184. [Google Scholar]
  20. Tu Y, Chen K, Wang H Y, et al. Regional water resources security evaluation based on a hybrid fuzzy BWM-TOPSIS method[J]. International Journal of Environmental Research and Public Health, 2020, 17(14): 4987. [Google Scholar]
  21. Wu Q H, Gao L, Gu X B. The assessment of water quality in the Ningxia Section of the Yellow River using intuitionistic fuzzy sets-TOPSIS model[J]. Polish Journal of Environmental Studies, 2022, 31(6): 5905-5914. [Google Scholar]
  22. An Y, Zou Z H, Li R R. Water quality assessment in the Harbin reach of the Songhuajiang River (China) based on a fuzzy rough set and an attribute recognition theoretical model[J]. International Journal of Environmental Research and Public Health, 2014, 11(4): 3507-3520. [Google Scholar]
  23. Ren L F, Zhang Y Q, Wang Y R, et al. Comparative analysis of a novel M-TOPSIS method and TOPSIS[J]. Applied Mathematics Research Express, 2007, 2007: abm005. [Google Scholar]
  24. Krohling R A, Pacheco A G. A-TOPSIS—An approach based on TOPSIS for ranking evolutionary algorithms[J]. Procedia Computer Science, 2015, 55: 308-317. [Google Scholar]
  25. Deng H P, Yeh C H, Willis R J. Inter-company comparison using modified TOPSIS with objective weights[J]. Computers & Operations Research, 2000, 27(10): 963-973. [Google Scholar]
  26. Li X X, Wang K S, Liu L W, et al. Application of the entropy weight and TOPSIS method in safety evaluation of coal mines[J]. Procedia Engineering, 2011, 26: 2085-2091. [Google Scholar]
  27. Hsu L C. Investment decision making using a combined factor analysis and entropy-based TOPSIS model[J]. Journal of Business Economics and Management, 2013, 14(3): 448-466. [Google Scholar]
  28. Li Y C, Zhao L, Suo J J. Comprehensive assessment on sustainable development of highway transportation capacity based on entropy weight and TOPSIS[J]. Sustainability, 2014, 6(7): 4685-4693. [Google Scholar]
  29. Ding L, Shao Z F, Zhang H C, et al. A comprehensive evaluation of urban sustainable development in China based on the TOPSIS-entropy method[J]. Sustainability, 2016, 8(8): 746. [Google Scholar]
  30. Li W W, Yi P T, Zhang D N. Sustainability evaluation of cities in Northeastern China using dynamic TOPSIS-entropy methods[J]. Sustainability, 2018, 10(12): 4542. [Google Scholar]
  31. Gu T, Ren P Y, Jin M Z, et al. Tourism destination competitiveness evaluation in Sichuan Province using TOPSIS model based on information entropy weights[J]. Discrete and Continuous Dynamical Systems-S, 2019, 12(4&5): 771-782. [Google Scholar]
  32. Li M, Sun H, Singh V P, et al. Agricultural water resources management using maximum entropy and entropy-weight-based TOPSIS methods[J]. Entropy, 2019, 21(4): 364. [Google Scholar]
  33. Shannon C E. A mathematical theory of communication[J]. The Bell System Technical Journal, 1948, 27(3): 379-423. [CrossRef] [Google Scholar]
  34. Martin M. Cubic spline interpolation of continuous functions[J]. Journal of Approximation Theory, 1974, 10(2): 103-111. [Google Scholar]
  35. McKinley S, Levine M. Cubic spline interpolation[J]. College of the Redwoods, 1998, 45(1): 1049-1060. [Google Scholar]
  36. Dierckx P. A fast algorithm for smoothing data on a rectangular grid while using spline functions[J]. SIAM Journal on Numerical Analysis, 1982, 19(6): 1286-1304. [Google Scholar]
  37. Hwang C L, Yoon K. Multiple Attribute Decision Making: Methods and Applications[M]. Berlin: Springer-Verlag, 1981. [Google Scholar]
  38. Yoon K, Hwang C L. Multiple attribute decision making[J]. European Journal of Operational Research, 1995, 4(4): 287-288. [Google Scholar]
  39. Spearman C. The proof and measurement of association between two things[J]. The American Journal of Psychology, 1987, 100(3/4): 441-471. [Google Scholar]
  40. Kendall M G. Rank Correlation Methods[M]. 4th Edition. London: Griffin, 1970. [Google Scholar]
  41. Chiang Y M, Hsieh H H. The use of the Taguchi method with grey relational analysis to optimize the thin-film sputtering process with multiple quality characteristic in color filter manufacturing[J]. Computers & Industrial Engineering, 2009, 56(2): 648-661. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.