M. Chandra Sekhar 1 , S. Manvitha 2
1 Professor, Civil Engineering Department, National Institute of Technology, Warangal, Telangana, India
2 Former M.Tech Student, National Institute of Technology, Warangal, Telangana, India
DOI: https://doi.org/10.47884/jweam.v3i2pp20-30
Received on: December 5, 2021 Revised on: February 10, 2022
Accepted on: March 22, 2022, Published on: April 30, 2022
ABSTRACT
Groundwater quality mapping is often a complex problem due to uneven distribution of monitoring
stations, variation in sampling frequency, hydrology, water quality, soil chemistry, etc. Numerical
modeling is commonly used for mapping groundwater quality. As an alternative, in the present study,
multivariate statistical approach is used to interpret ground water quality of Hussainsagar catchment area
of Hyderabad, India. Ground water quality data was collected from Telangana State Ground Water
Department which consists of analytical results from 2010 to 2016. Fourteen quality parameters are
considered for 25 sampling sites for both pre monsoon and post monsoon period separately. The dataset
was analysed using Cluster Analysis (CA) and Principle Component Analysis (PCA). CA formed three
groups of similarity among the sampling sites representing the different physicochemical characteristics
of the study area. Results of PCA showed that, four factors were responsible for the data structure
explaining 78% of the total variance of the dataset in pre monsoon period and are identified as soil
leaching, industrial, natural and anthropogenic Pollution. Similarly, principle components were identified
as responsible for the data structure explaining 80% of the total variance in the post monsoon period and
are described as soil leaching, industrial, natural pollution and weathering.
Keywords: Multivariate statistical analysis, Cluster Analysis, Principal Component Analysis,
Groundwater quality.
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