Groundwater is one of the world's most vital resources. It is commonly utilized in agriculture in India and provides drinking water to millions of people, particularly those living in rural regions. Pre- and post-monsoon data were gathered from the government's publicly available website for the years 2021 and 2022. The data includes twelve polluting components collected at 32 locations throughout Telangana's Yadadri Bhuvanagiri district. The data was subjected to principal component analysis (PCA) to better understand the interrelationships between physiochemical components in groundwater, as well as to investigate the contribution of each component to the Water Quality Index (WQI). The Geographic Information System (GIS) was used to better comprehend and display the relationship. TDS, Cl, SO4, Na, Ca, Mg, and TH created Principal Component 1, which accounted for roughly 50% of the variation in WQI using PCA analysis. Inverse Distance Weighted (IDW) interpolated GIS values corresponding to Principal components showed the same levels in different locations of the research area and were consistent with WQI levels.

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PCA and GIS Analysis of Groundwater Quality Over Yadadri

  • Nannaparaju Vasudha,
  • Polisetty Venkateswara Rao

摘要

Groundwater is one of the world's most vital resources. It is commonly utilized in agriculture in India and provides drinking water to millions of people, particularly those living in rural regions. Pre- and post-monsoon data were gathered from the government's publicly available website for the years 2021 and 2022. The data includes twelve polluting components collected at 32 locations throughout Telangana's Yadadri Bhuvanagiri district. The data was subjected to principal component analysis (PCA) to better understand the interrelationships between physiochemical components in groundwater, as well as to investigate the contribution of each component to the Water Quality Index (WQI). The Geographic Information System (GIS) was used to better comprehend and display the relationship. TDS, Cl, SO4, Na, Ca, Mg, and TH created Principal Component 1, which accounted for roughly 50% of the variation in WQI using PCA analysis. Inverse Distance Weighted (IDW) interpolated GIS values corresponding to Principal components showed the same levels in different locations of the research area and were consistent with WQI levels.