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Safavi, H. R., Ahmadi, A., Rahmatnia, M. (2015), River Water Quality Zoning Using Combination of Principal Component Analysis (PCA) and Fuzzy Clustering Analysis,Water and Wastewater, 25 (593), 21-31


Management decisions whose environmental impacts affect directly or indirectly surface waters must of

necessity be based on adequate knowledge and information when water quality zoning and a clear picture of

river water quality are sought. Water quality zoning is based on pollution criteria that are identified on the basis

of different water quality parameters drawn from historical data and the water uses in the region. The aggregate

of the data and parameters involved make river water quality modeling a complex process. In this paper, the

Principal Component Analysis (PCA) is used to reduce the water quality parameters involved in the

identification of river water pollution criteria. The method keeps those components with more variances. The

results show that the first component transfers 93.59% of the variation in the data, while the first two and the

first six components explain 96.67% and 99.99% of the variations, respectively. Based on the criteria thus

identified, the fuzzy clustering analysis is used in a second stage of the study to classify the river intervals. For

this purpose, the fuzzy water quality data are provided to generate the fuzzy similarity matrix based on the fuzzy

relations. Then, the stabilized matrix and the clustering diagram are created. Finally, the river intervals are

classified into similar categories using the proper thresholds. The efficiency of the proposed method is evaluated

by employing water quality data collected from the Zayandehrood River monitoring stations.


Keywords: Water Quality Zoning, River Water Quality, Principal Component Analysis (PCA), Fuzzy

Clustering Analysis (FCA).


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