Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11629
Title: Application of interval clustering approach to water quality evaluation
Authors: Wong, H 
Hu, BQ
Issue Date: 2013
Source: Journal of hydrology, 2013, v. 491, no. 1, p. 1-12
Abstract: The extension evaluation method (EEM) has been applied to evaluate water quality. However, in real-life applications, sample data may be given as intervals because of errors produced in measurement, poor data brought by poor information, and imprecise data induced by human errors. To deal with data set in the form of a number of intervals, the interval extension evaluation method (IEEM) has been previously introduced. However, the correlative degrees that are obtained from IEEM may yield negative numbers. In evaluating water quality one generally assigns ranks or grades that are non-negative. Then it is expected that correlative degrees must be non-negative. This paper provides a novel method, i.e., the interval clustering approach (ICA), which is based on the grey clustering approach (GCA) and interval-valued fuzzy sets, to overcome this negativity issue. The method also introduces the notion of weightings in the form of intervals, by which interval samples can be analyzed with a view to delineating the important attributes via the interval weights. To demonstrate our proposed approach, the ICA is applied to evaluate the water quality of three different cross-sections of the Fen River, the second major branch river of the Yellow River in China. Our proposed method is a useful tool for the analysis of poorly measured data, poorly collected data and imprecise hydrological data which are very commonly encountered in water research. In brief, our method is novel for analyzing interval data. The GCA is a special case of the ICA, as these intervals are degenerated single values.
Keywords: Grey clustering approach (GCA)
Interval-valued fuzzy set
Water quality criteria
Water quality evaluation (WQE)
Water quality index (WQI)
Publisher: Elsevier Science Bv
Journal: Journal of Hydrology 
ISSN: 0022-1694
DOI: 10.1016/j.jhydrol.2013.03.009
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