Neural Horizons: The Uncharted Future of Deep Learning
Articles

Gaussian Process Regression Analysis Using Historical Data to Forecast Water Quality: A Case Study

PDF
  • Abhijit Paul,
  • Khushi Bhalla
Abhijit Paul
Department of Computer Science, Swami Vivekananda University, Barrackpore, India
Khushi Bhalla
Department of Information Technology, Amity University, Kolkata, India

Published 2026-05-27

Keywords

  • Water quality Prediction,
  • Gaussian Process Regression,
  • pH Prediction,
  • Time Series analysis,
  • Water quality management,
  • Non- parametric Methods,
  • Machine Learning
    • ...More
    Less

How to Cite

Paul, Abhijit, and Khushi Bhalla. “Gaussian Process Regression Analysis Using Historical Data to Forecast Water Quality: A Case Study”. Swami Vivekananda University Press, May 2026, pp. 17-25, https://swamivivekanandauniversitypress.com/index.php/svup/article/view/554.

Copyright (c) 2026 Swami Vivekananda University Press

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

In terms of public health and environmental monitoring, water quality is crucial. Water resource safety and proactive management depend on accurate estimation of water quality parameters. This research investigates the use of Gaussian Process Regression (GPR) to forecast water quality using historical data over the previous five years. GPR is a perfect tool for environmental modelling because of its probabilistic character, which not only makes precise predictions but also allows for the assessment of uncertainty. The study contains visual representations of the model’s performance, a comparison of prediction results with actual values, and a thorough investigation of several water quality metrics.

DOI - https://doi.org/10.65525/SVUP.9788199778009.2026.17-25

PDF