In this paper, classification of a large hydrochemical data set from Varamin plain is done by using fuzzy c-means (FCM) and hierarchical cluster analysis (HCA) clustering techniques. Then its application to hydrochemical facies delineation is discussed. Groundwater samp More
In this paper, classification of a large hydrochemical data set from Varamin plain is done by using fuzzy c-means (FCM) and hierarchical cluster analysis (HCA) clustering techniques. Then its application to hydrochemical facies delineation is discussed. Groundwater samples were grouped into three classes according to the optimum number of the classes and fuzziness exponent by using the fuzzy c-mean. The data set includes 90 deep and moderate deep well samples from groundwater data set and 9 hydrochemical variables were used. Results from both FCM and HCA clustering produced cluster centers that can be used to identify the physical and chemical processes creating the variations in the water chemistries. The optimum cluster in FCM method determined by optimization function, but in HCA method by trial and error. The FCM method is potentially useful in establishing hydrochemical facies distribution and may provide a better tool than HCA for clustering large data sets when overlapping or continuous clusters exist. Plotting the cluster membership value contours on a map demonstrated the existence of three spatially continuous, well-defined clusters of groundwater samples. The results showed that the FCM method is more sound for investigating threshold data rather than HCA method (that represents sharp and abrupt variations).
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