Author(s)

Taqveem Ali Khan^*

Department of Geology, Aligarh Muslim University, Aligarh, India.

A quantitative analytical data from the alluvial aquifers of Ganga-Sot Sub-Basin (GSSB) is subjected to multivariate statistical analysis in order to ascertain the groundwater quality characterization depending on the top soil/physiographic divisions. The matrix consists of ten variables of 34 groundwater samples collected from evenly spaced locations. The Hierarchical cluster analysis resulted in six clusters. Each cluster group is individually subjected to Principal component analysis (PCA). PCA of group A explains cumulative variance of 83%, and 95% of group of B, 82% of group C, 91% of group of D. Dissimilarity among the clusters is due to anthropogenic influence on the groundwater regime. The PCA is done for the groundwater quality data of the whole area and on the data of sub-divided area. The PCA of the whole area resulted in five components with cumulative variance of 69.19%. The area is sub divided on the basis of soil type/physiography and data falling in each sub-division is subjected to PCA. The PCA of clay loam soil/Ganga Mahwa low land resulted in five PCs explaining cumulative variance of 91%. The PCA of sandy soil/central upland data extracted four PCs with 80% of cumulative variance. The PCA of loam type of soil/Sot plain extracted three PCs explaining cumulative variance of 91.751%. The three physiographic units of the alluvium setting reflect distinct groundwater quality as manifested by the PCA. From this study it can be ascertained that PCA can be used for the characterization of groundwater quality information.

Principal Component Analysis, Hierarchical Cluster Analysis, Dendrogram, Aquifers

Khan, T. (2015) Groundwater Quality Evaluation Using Multivariate Methods, in Parts of Ganga Sot Sub-Basin, Ganga Basin, India. Journal of Water Resource and Protection, 7, 769-780. doi: 10.4236/jwarp.2015.79063.