Water Pollution Prediction Using Machine Learning : Water is Potable or not

Deepa jaiswal , Amit kumar pandey , Santosh kumar singh

Water Quality Prediction, Machine Learning, Artificial Intelligence, Water Potability, Support Vector Classifier, Random Forest Classifier.

Clean and safe drinking water is a fundamental human need, but water pollution is a severe environmental and public health problem. Laboratory chemical and biological analysis is the traditional method for estimating water quality, which is expensive, time-consuming, and not available at remote locations away from the analytical laboratory. Applying quantifiable physicochemical properties such as pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity, this research advocates for a machine learning method reliant on artificial intelligence to predict water potability. Supervised machine learning algorithms like Random Forest, Naïve Bayes, K-Nearest Neighbors, Support Vector Classifier, Logistic Regression, and Decision Tree were evaluated for prediction performance. Models were highly accurate, and Support Vector Classifier contains a maximum of 68.85%. The article assumes the potential of AI-based models being efficient, scalable, and cost-effective solutions to water quality estimation with immense possibility for real-time measurement and sustainable management of water resources.

Published Paper   E-Certificate

"Water Pollution Prediction Using Machine Learning : Water is Potable or not", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 3, page no.b418-b424, March-2025, Available :https://ijsdr.org/papers/IJSDR2503151.pdf

Volume 10 Issue 3, March-2025

Pages : b418-b424

Paper Reg. ID: IJSDR_301105

Published Paper Id: IJSDR2503151

Downloads: 000149

Research Area: Science All

Country: Mumbai, Maharastra, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2503151

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2503151

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition