Nishant Khare, Priyanka Asthana | International Journal of Water Resources Engineering | Vol 11, Issue 01 | pp. 33-42 | ISSN: 2456-1606
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
APA
Khare, N., & Asthana, P. (2025). An Efficient Machine Learning System forPrediction of Water Quality with Explainable AI. International Journal of Water Resources Engineering, 11(01), 33-42.MLA
Khare, Nishant, and Priyanka Asthana. “An Efficient Machine Learning System forPrediction of Water Quality with Explainable AI.” International Journal of Water Resources Engineering, vol. 11, no. 01, 2025, pp. 33-42.Chicago
Nishant Khare, and Priyanka Asthana. “An Efficient Machine Learning System forPrediction of Water Quality with Explainable AI.” International Journal of Water Resources Engineering 11, no. 01 (2025): 33-42.Vancouver
Khare N, Asthana P. An Efficient Machine Learning System forPrediction of Water Quality with Explainable AI. International Journal of Water Resources Engineering. 2025;11(01):33-42.BibTeX
@article{KhareN2025,
author = {Nishant Khare and Priyanka Asthana},
title = {An Efficient Machine Learning System forPrediction of Water Quality with Explainable AI},
journal = {International Journal of Water Resources Engineering},
year = {2025},
volume = {11},
number = {01},
pages = {33--42},
issn = {2456-1606},
url = {https://journalspub.com/publication/ijwre/article=15008}
}