Evaluation of graphical and multivariate statistical methods for classification of water chemistry data |
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Authors: | Cüneyt Güler Geoffrey D Thyne John E McCray Keith A Turner |
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Institution: | (1) Colorado School of Mines, Department of Geology and Geological Engineering, 1500 Illinois Street, Golden, CO 80401, USA, |
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Abstract: | A robust classification scheme for partitioning water chemistry samples into homogeneous groups is an important tool for the
characterization of hydrologic systems. In this paper we test the performance of the many available graphical and statistical
methodologies used to classify water samples including: Collins bar diagram, pie diagram, Stiff pattern diagram, Schoeller
plot, Piper diagram, Q-mode hierarchical cluster analysis, K-means clustering, principal components analysis, and fuzzy k-means
clustering. All the methods are discussed and compared as to their ability to cluster, ease of use, and ease of interpretation.
In addition, several issues related to data preparation, database editing, data-gap filling, data screening, and data quality
assurance are discussed and a database construction methodology is presented.
The use of graphical techniques proved to have limitations compared with the multivariate methods for large data sets. Principal
components analysis is useful for data reduction and to assess the continuity/overlap of clusters or clustering/similarities
in the data. The most efficient grouping was achieved by statistical clustering techniques. However, these techniques do not
provide information on the chemistry of the statistical groups. The combination of graphical and statistical techniques provides
a consistent and objective means to classify large numbers of samples while retaining the ease of classic graphical presentations.
Electronic Publication |
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Keywords: | Classification techniques Cluster analysis Database construction Fuzzy k-means clustering Water chemistry |
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