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November 17, 2025Environmental Quality ManagementOpen Access

A Hybrid Approach Based on Principal Component Analysis and Artificial Neural Network for Modeling River Water Quality of Prayagraj Region – An Important Pilgrimage Site of India

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Authors

ALAnurag Samson LallAPAvinash Kumar PandeyJMJyoti Vandana Mani

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Overview

This investigation found that PCA-ANN effectively predicts water quality index in river water, indicating its potential for policy application.

Key Points

  • To model the river water quality in the Prayagraj region using advanced computational techniques.
  • Collected 100 river water samples from five sites in Prayagraj between January 2020 and November 2021.
  • Analyzed 12 physicochemical parameters, including turbidity and hardness.
  • Evaluated the overall water quality index using weighted arithmetic methods, and applied PCA to reduce data dimensionality.
  • Mean turbidity exceeded BIS permissible limits at 6 ± 3.65 NTU.
  • Overall water quality index (WQI) was 66.39 ± 23.35, indicating unfit for human consumption.
  • PCA-ANN model achieved high prediction accuracy (R2 = 0.998), demonstrating its effectiveness for rapid WQI evaluation.

Cite This Study

Lall et al. (2025) studied this question.

synapsesocial.com/papers/692509fbc0ce034ddc352fedhttps://doi.org/10.1002/tqem.70229
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