An Efficient Machine Learning System forPrediction of Water Quality with Explainable AI

Volume: 11 | Issue: 01 | Year 2025 | Subscription
International Journal of Water Resources Engineering
Received Date: 01/15/2025
Acceptance Date: 01/26/2025
Published On: 2025-02-03
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By: Nishant Khare and Priyanka Asthana

1Student,Computer Science and Engineering ,Lakshmi Narain College of Technology, Bhopal, India
2Professor ,Computer Science and Engineering ,Lakshmi Narain College of Technology, Bhopal, India

Abstract

Abstract

Water availability and quality have been issues globally, especially in developing nations. Unchecked urbanization and industrialization contaminate natural water supplies and aquifers, potentially introducing physical, chemical, and biological contaminants. Over 80% of illnesses are caused by drinking tainted water, according to a WHO assessment. As clean water sources become more at risk, water quality protection has become of human, environmental, and economic importance worldwide. These water sources, crucial for thousands of communities that draw their water from them, have become increasingly polluted by industrial effluent, farmer’s leachate and expanding urban development. The skilled factors such as; pH, turbidity, temperature and total hardness act as parameters to check water quality. AI and ML are truly game-changers in water quality monitoring, given that by considering big data, they allow the early prediction of contamination levels. LIME techniques are a subset of the broader Explainable AI (XAI) techniques which aim at increasing interpretability by explaining what a model has decided. Such developments promote better management of water resources and improve the quality of water in the environment.

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How to cite this article: Nishant Khare and Priyanka Asthana, An Efficient Machine Learning System forPrediction of Water Quality with Explainable AI. International Journal of Water Resources Engineering. 2025; 11(01): -p.

How to cite this URL: Nishant Khare and Priyanka Asthana, An Efficient Machine Learning System forPrediction of Water Quality with Explainable AI. International Journal of Water Resources Engineering. 2025; 11(01): -p. Available from:https://journalspub.com/publication/ijwre/article=15008

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