Abstract
The application of statistical techniques for the study of groundwater data provides an authentic understanding of aquifer and ecological condition and leads to the recognition of the potential sources that determine the groundwater system. The groundwater quality data generated was analyzed using Principal Component Analysis and Cluster analysis. Principal Component Analysis result yielded five principal factor which accounted for 78.69% total variance dominated by Total Hardness, Total Dissolved Solids, Cu, Cl−, NO3−, Cu, Electrical Conductivity indicating that the major variations are related to human actions and natural processes. Cluster analysis grouped the 20 boreholes into four statistically substantial clusters.
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Acknowledgements
This is to acknowledge the Managements of the Tertiary Education Trust Fund (TETFund) Abuja, Nigeria and Binyaminu Usman Polytechnic, Hadejia, Jigawa State for sponsorship and approving to attend this conference in cash and kind, I am very much grateful.
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Garba, A., Idris, A.M., Gambo, J. (2023). Groundwater Quality Assessment Using Principal Component and Cluster Analysis. In: Sherif, M., Singh, V.P., Sefelnasr, A., Abrar, M. (eds) Water Resources Management and Sustainability. Water Science and Technology Library, vol 121. Springer, Cham. https://doi.org/10.1007/978-3-031-24506-0_22
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