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- فارسی
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
Abstract
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).