Pulse Brain · Growing Health Evidence Index
Peer-reviewed

Reliable water quality prediction and parametric analysis using explainable AI models

M. K. Nallakaruppan; E. Gangadevi; M. Lawanya Shri; Balamurugan Balusamy; Sweta Bhattacharya; Shitharth Selvarajan

Scientific Reports · 2024

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Summary

The consumption of water constitutes the physical health of most of the living species and hence management of its purity and quality is extremely essential as contaminated water has to potential to create adverse health and environmental consequences. This creates the dire necessity to measure, control and monitor the quality of water. The primary contaminant present in water is Total Dissolved Solids (TDS), which is hard to filter out. There are various substances apart from mere solids such as potassium, sodium, chlorides, lead, nitrate, cadmium, arsenic and other pollutants. The proposed work aims to provide the automation of water quality estimation through Artificial Intelligence and uses Explainable Artificial Intelligence (XAI) for the explanation of the most significant parameters

Source type
Peer-reviewed study
DOI
10.1038/s41598-024-56775-y
Catalogue ID
NRmo9zxr64-07h
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