Comparative Study of Hydrochemical Classification Based on Different Hierarchical Cluster Analysis Methods

Jianwei Bu, Wei Liu, Zhao Pan and Kang Ling
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Jianwei Bu: School of Environmental Studies, China University of Geosciences, No. 68 Jincheng Street, Wuhan 430078, China
Wei Liu: Institute of Geological Survey, China University of Geosciences, No. 388 Lumo Road, Wuhan 430074, China
Zhao Pan: School of Environmental Studies, China University of Geosciences, No. 68 Jincheng Street, Wuhan 430078, China
Kang Ling: School of Environmental Studies, China University of Geosciences, No. 68 Jincheng Street, Wuhan 430078, China

IJERPH, 2020, vol. 17, issue 24, 1-23

Abstract: Traditional methods for hydrochemical analyses are effective but less diversified, and are constrained to limited objects and conditions. Given their poor accuracy and reliability, they are often used in complement or combined with other methods to solve practical problems. Cluster analysis is a multivariate statistical technique that extracts useful information from complex data. It provides new ideas and approaches to hydrogeochemical analysis, especially for groundwater hydrochemical classification. Hierarchical cluster analysis is the most widely used method in cluster analysis. This study compared the advantages and disadvantages of six hierarchical cluster analysis methods and analyzed their objects, conditions, and scope of application. The six methods are: The single linkage, complete linkage, median linkage, centroid linkage, average linkage (including between-group linkage and within-group linkage), and Ward’s minimum-variance. Results showed that single linkage and complete linkage are unsuitable for complex practical conditions. Median and centroid linkages likely cause reversals in dendrograms. Average linkage is generally suitable for classification tasks with multiple samples and big data. However, Ward’s minimum-variance achieved better results for fewer samples and variables.

Keywords: groundwater leakage; hydrochemical classification; multivariate statistics; hierarchical cluster analysis; Bayi Tunnel (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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