Analyzing Factors Affecting the Water Quality of the Ganges and Predicting Further Patterns

International Journal of Computer Science and Engineering

© 2025 by SSRG - IJCSE Journal

Volume 12 Issue 3

Year of Publication : 2025

Authors : Arush Nath

10.14445/23488387/IJCSE-V12I3P101

How to Cite?

Arush Nath, "Analyzing Factors Affecting the Water Quality of the Ganges and Predicting Further Patterns," SSRG International Journal of Computer Science and Engineering , vol. 12,  no. 3, pp. 1-9, 2025. Crossref, https://doi.org/10.14445/23488387/IJCSE-V12I3P101

Abstract:

Observing the degrading water quality in India and the lack of established forecasting methods for the same, this study aimed to use publicly available data from the CPCB website to test different machine learning algorithms and see which one is most viable for prediction of water quality metrics - specifically D.O. This study carried out comparisons using R2 Score between numerous machine learning algorithms such as XGBoost, Support Vector Regressor, Gradient Boosting, Random Forest, and Linear Regression. The study concluded that the most accurate prediction model was Random Forest, with an R2 Score of 0.76, making it a viable means of forecasting future patterns in water quality metrics.

Keywords:

Dissolved Oxygen (D.O.), Machine Learning, CPCB, Hyperparameter, Forecast.

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