Interpretation of Observed Surface Ponds Water Quality using Principal Components Analysis and Cluster Analysis

A. O. Ayeni


Varieties of approaches are being used to interpret the concealed variables that determine the variance of observed water quality of various sources. A considerable proportion of these approaches are statistical methods, multivariate statistical techniques in particular. The use of multivariate statistical techniques is required when the number of variables is large and greater than two for easy and robust evaluation. By means of multivariate statistics of principal components analysis (PCA) and cluster analysis (CA), this study attempted to determine major factors responsible for the variations in the quality of 30 surface ponds used for domestic purposes in the 6 selected communities of Akoko Northeast LGA, Ondo State, Nigeria. It classifies the samples’ location into mutually exclusive unknown groups that share similar characteristics/properties. The laboratory results of 20 parameters comprising 6 physicals, 8 chemicals, 4 heavy metals and 2 microbial from the sampled ponds were subjected to PCA and CA for further interpretation. The result shows that 5 components account for 97.52% of total variance of the surface pond quality while 2 cluster groups were identified for the locations. Based on the parameters concentrations and the land uses impacts, it was concluded that domestic and agricultural waste strongly influenced the variation and the quality of ponds in the area.


Multivariate statistics, ponds, water quality, variance and interpretation

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