Reliable water quality prediction and parametric analysis using explainable AI models
Vellore Institute of Technology University · Shiv Nadar University · +2 more institutions
Abstract
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…
Citation impact
- FWCI
- 21.78
- Percentile
- 100%
- References
- 67
Authors
6Topics & keywords
- Decision tree
- Support vector machine
- Random forest
- Computer science
- Artificial intelligence
- Water quality
- Machine learning
- Naive Bayes classifier
- Clean water and sanitation