Background: Evaluating water quality is a major environmental challenge, especially when dealing with large datasets with many linked parameters. Traditional evaluation approaches can become convoluted and ineffective, hindering prompt understanding of pollution patterns. Objective: This study uses Principal Component Analysis (PCA) to improve water quality assessment by lowering the number of data dimensions and finding the most important factors. This makes monitoring more accurate and efficient. Methodology: Water samples were collected from 23 monitoring stations across Hyderabad and analyzed for 16 water-quality parameters. PCA was used to determine relationships among variables to reduce redundancy. A total of 9 principal parameters were obtained through the dimensionality reduction process, which included: TSS, F−, NO3 −, PO4 3−, B, Na, K, FC, and TC, explaining 87.78% of the total variance. Results: The PCA-based framework made the data much less complicated while preserving the important information needed to evaluate water quality. The found parameters facilitated a more efficient evaluation of pollution patterns at monitoring sites in comparison to conventional full-parameter analyses. Conclusion: This study demonstrates that PCA is an effective instrument for streamlining water quality monitoring by emphasizing the most critical elements. The results set the stage for adding PCA to systems that automatically and in real time keep an eye on things. By combining PCA with machine learning techniques, future advancements could strengthen predictive modeling capabilities for improved management of water resources.
Mohanty et al. (Tue,) studied this question.
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