References

  1. Z. Zou, Y. Yun, J. Sun, Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment, J. Environ. Sci., 18 (2006) 1020–1023.
  2. H. Boyacioglu, H. Boyacioglu, Detection of seasonal variations in surface water quality using discriminant analysis, Environ. Monit. Assess., 162 (2010) 15–20.
  3. S. Shrestha, F. Kazama, T. Nakamura, Use of principal component analysis, factor analysis and discriminant analysis to evaluate spatial and temporal variations in water quality of the Mekong River, J. Hydroinf., 10 (2008) 43–56.
  4. X. Xin, W. Lu, L. Gong, Discriminant analysis method application in water quality assessment, Environ. Sci. Technol., 31 (2008) 113–115.
  5. W. Lu, J. Li, F. Yu, G. Yu, L. Liu, Application of step wise discriminant analytical method in screening factor in the water quality evaluation, J. Jilin Univ., 39 (2009) 126–30.
  6. A. Papaioannou, A. Mavridou, C. Hadjichristodoulou, P. Papastergiou, O. Pappa, E. Dovriki, I. Rigas, Application of multivariate statistical methods for groundwater physicochemical and biological quality assessment in the context of public health, Environ. Monit. Assess., 170 (2010) 87–97.
  7. S. Kamble, R. Vijay, Assessment of water quality using cluster analysis in coastal region of Mumbai, India, Environ. Monit. Assess., 178 (2011) 321–332.
  8. L. Zadeh, Fuzzy sets, Inf. Control, 8 (1965) 338–353.
  9. X. Wang, Z. Zou, H. Zou, Water quality evaluation of Haihe River with fuzzy similarity measure methods, J. Environ. Sci., 25 (2013) 2041–2046.
  10. S.C. Jiang, S.B. Ge, X. Wu, Y.M. Yang, J.T. Chen, W.X. Peng, Treating n-butane by activated carbon and metal oxides, Toxicol. Environ. Chem., 99 (2017) 753–759.
  11. Q. Wang, Z. Zou, Application of BP neural network in water quality assessment for Miyun reservoir recharged with reclaimed water, Acta Sci. Circumstantiae, 34 (2014) 2413–2416.
  12. T. He, J. Li, H. Huang, Water quality evaluation of RBF neural network based on optimized parameter of genetic algorithm, Comput. Eng., 37 (2011) 13–15.
  13. Y. Hu, H. Wang, A new data mining method based on huge data and its application, J. Beijng Univ. Aeronaut. Astronaut., 17 (2004) 40–44.
  14. H.H. Bock, E. Diday, Analysis of Symbolic Data, Springer- Verlag, New York, NY, 2000.
  15. W. Li, J. Guo, Methodology and application of regression analysis of interval-type symbolic data, J. Manage. Sci. China, 33 (2010) 38–43.
  16. A. Jain, M. Murty, P. Flynn, Data clustering: a review, ACM Comput. Surv., 31 (1999) 264–323.
  17. A. Gordon, Classification, Chapman and Hall, Boca Raton, FL, 1999.
  18. A. Sharma, R. Ganguly, A.K. Gupta, Impact assessment of leachate pollution potential on groundwater: an indexing method, J. Environ. Eng., 146 (2020) 116–131.
  19. R. Rana, R. Ganguly, A.K. Gupta, Indexing method for assessment of pollution potential of leachate from non-engineered landfill sites and its effect on ground water quality, Environ. Monit. Assess., 190 (2018) 1–23.
  20. A. Gibrilla, E.K.P. Bam, D. Adomako, S. Ganyaglo, S. Osae, T.T. Akiti, S. Kebede, E. Achoribo, E. Ahialey, G. Ayanu, E.K. Agyeman, Application of water quality index (WQI) and multivariate analysis for groundwater quality assessment of the Birimian and cape Coast Granitoid Complex: Densu River Basin of Ghana, Water Qual. Exposure Health, 3 (2011) 63–78.
  21. C. Güler, G.D. Thyne, J.E. McCray, K.A. Turner, Evaluation of graphical and multivariate statistical methods for classification of water chemistry data, Hydrogeol. J., 10 (2002) 455–474.
  22. S. Manikandan, S. Chidambaram, A.L. Ramanathan, M.V. Prasanna, U. Karmegam, C. Singaraja, P. Paramaguru, I. Jainab, A study on the high fluoride concentration in the magnesium-rich waters of hard rock aquifer in Krishnagiri district, Tamilnadu, India, Arabian J. Geosci., 7 (2014), 273–285.
  23. H. Liu, Z. Liu, Recycling utilization patterns of coal mining waste in China, Resour. Conserv. Recycl., 54 (2010) 1331–1340.
  24. L. Zhang, Y. Jia, L. Zhang, H. He, C. Yang, M. Luo, L. Miao, Preparation of soybean oil factory sludge catalyst by plasma and the kinetics of selective catalytic oxidation denitrification reaction, J. Cleaner Prod., 217 (2019) 317–323.
  25. H. Wang, H. Zhong, G. Bo, Existing forms and changes of nitrogen inside of horizontal subsurface constructed wetlands, Environ. Sci. Pollut. Res., 25 (2018) 771–781.
  26. E. Diday, F. Brito, Symbolic Cluster Analysis, O. Opitz, Eds., Conceptual and Numerical Analysis of Data, Springer-Verlag, Heidelberg, 1989, pp. 45–84.
  27. F. Carvalho, M. Csernel, Y. Lechevallier, Clustering constrained symbolic data, Pattern Recognit. Lett., 30 (2009) 1037–1045.
  28. F. Carvalho, P. Brito, H.H. Bock, Dynamic clustering for interval data based on l2 distance, Comput. Stat., 21 (2006) 231–250.
  29. C. Tenorio, F. Carvalho, J. Pimentel, A Partitioning Fuzzy Clustering Algorithm for Symbolic Interval Data Based on Adaptive Mahalanobis Distances, Proceedings of 7th International Conference on Hybrid Intelligent Systems, Kaiserlautern, 2007, pp. 174–179.
  30. F. Carvalho, C. Tenorio, Fuzzy k-means clustering algorithms for interval-valued data based on adaptive quadratic distances, Fuzzy Sets Syst., 161 (2010) 2978–2999.
  31. F. Carvalhoa, Y. Lechevallierb, Partitional clustering algorithms for symbolic interval data based on single adaptive distances, Pattern Recognit., 42 (2009) 1223–1236.
  32. A. Irpino, R. Verde, Dynamic clustering of interval data using a wasserstein-based distance, Pattern Recognit., 29 (2008) 1648–1658.
  33. S. Ren, J. Lv, Genetic algorithm-based kernel function FCM clustering algorithm for interval numbers, J. Syst. Eng., 23 (2008) 611–616.
  34. C. Yu, Z. Fan, A FCM cluster algorithm for multiple attribute information with interval numbers, Oper. Res. Manage. Sci., 13 (2010) 12–16.
  35. W. Li, H. Dai, J. Guo, Hierarchical clustering of generally distributed interval symbolic data, J. Appl. Stat. Manage., 32 (2013) 1071–1078.
  36. A.M. Danby, M.D. Lundin, B. Subramaniam, Valorization of grass lignins: swift and selective recovery of pendant aromatic groups with ozone, ACS Sustainable Chem. Eng., 6 (2018) 71–76.