Multivariate statistical techniques, including Seasonal Indices and principal component analysis (PCA) were used to evaluate the seasonal variations and to interpret a critical season which is responsible for the variation in water quality data sets collected from the Wular Lake in Kashmir. The data sets, which contained 10 parameters for the four seasons Winter, Spring, Summer & Autumn were collected during a year monitoring program at 5 different sites along the Lake. Variation were found for the four season by Seasonal Indices and by the PCA the season spring is highly processed and value added for the variation in the quality of water whereas the summer & Autumn is moderately responsible for the changes in the water quality and winter are not value added. Furthermore, this study revealed that the major cause of water quality changes is due to Season.By Dr. Smita Jain" Identifying the Most Critical Season for Variation in Water Quality by using Principal Component Analysis"
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017,
Paper URL: http://www.ijtsrd.com/papers/ijtsrd2511.pdf
Direct URL: http://www.ijtsrd.com/other-scientific-research-area/enviormental-science/2511/identifying-the-most-critical-season-for-variation-in--water-quality-by-using-principal-component-analysis/dr-smita-jain
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