References
- 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.
- H. Boyacioglu, H. Boyacioglu, Detection of seasonal variations
in surface water quality using discriminant analysis, Environ.
Monit. Assess., 162 (2010) 15–20.
- 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.
- X. Xin, W. Lu, L. Gong, Discriminant analysis method
application in water quality assessment, Environ. Sci. Technol.,
31 (2008) 113–115.
- 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.
- 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.
- S. Kamble, R. Vijay, Assessment of water quality using cluster
analysis in coastal region of Mumbai, India, Environ. Monit.
Assess., 178 (2011) 321–332.
- L. Zadeh, Fuzzy sets, Inf. Control, 8 (1965) 338–353.
- X. Wang, Z. Zou, H. Zou, Water quality evaluation of Haihe
River with fuzzy similarity measure methods, J. Environ. Sci.,
25 (2013) 2041–2046.
- 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.
- 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.
- 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.
- 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.
- H.H. Bock, E. Diday, Analysis of Symbolic Data, Springer-
Verlag, New York, NY, 2000.
- W. Li, J. Guo, Methodology and application of regression
analysis of interval-type symbolic data, J. Manage. Sci. China,
33 (2010) 38–43.
- A. Jain, M. Murty, P. Flynn, Data clustering: a review,
ACM Comput. Surv., 31 (1999) 264–323.
- A. Gordon, Classification, Chapman and Hall, Boca Raton,
FL, 1999.
- 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.
- 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.
- 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.
- 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.
- 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.
- H. Liu, Z. Liu, Recycling utilization patterns of coal
mining waste in China, Resour. Conserv. Recycl., 54 (2010)
1331–1340.
- 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.
- 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.
- E. Diday, F. Brito, Symbolic Cluster Analysis, O. Opitz, Eds.,
Conceptual and Numerical Analysis of Data, Springer-Verlag,
Heidelberg, 1989, pp. 45–84.
- F. Carvalho, M. Csernel, Y. Lechevallier, Clustering constrained
symbolic data, Pattern Recognit. Lett., 30 (2009) 1037–1045.
- F. Carvalho, P. Brito, H.H. Bock, Dynamic clustering for
interval data based on l2 distance, Comput. Stat., 21 (2006)
231–250.
- 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.
- 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.
- F. Carvalhoa, Y. Lechevallierb, Partitional clustering algorithms
for symbolic interval data based on single adaptive distances,
Pattern Recognit., 42 (2009) 1223–1236.
- A. Irpino, R. Verde, Dynamic clustering of interval data using
a wasserstein-based distance, Pattern Recognit., 29 (2008)
1648–1658.
- S. Ren, J. Lv, Genetic algorithm-based kernel function FCM
clustering algorithm for interval numbers, J. Syst. Eng.,
23 (2008) 611–616.
- C. Yu, Z. Fan, A FCM cluster algorithm for multiple attribute
information with interval numbers, Oper. Res. Manage. Sci.,
13 (2010) 12–16.
- W. Li, H. Dai, J. Guo, Hierarchical clustering of generally
distributed interval symbolic data, J. Appl. Stat. Manage.,
32 (2013) 1071–1078.
- 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.