Data - driven Explainable Predictive Features For Water Quality Prediction Using Ensemble Learning
Abstract & Details
Research Area
Sri Venkatesa Perumal college of Engineering & Technology
Keywords
Water quality prediction
Ensemble learning
Water contamination
Water resource management
Predictive modeling
Environmental monitoring
Data analysis
Machine learning
Model accuracy
Over-estimation in models.
Abstract
The deteriorating quality of natural water resources like lakes streams and estuaries is one of the direst and most worrisome issues faced by humanity. The effects of un-clean water are far-reaching impacting every aspect of life. Therefore management of water resources is very crucial in order to optimize the quality of water. The effects of water contamination can be tackled efficiently if data is Analyzed and water quality is predicted beforehand.This issue has been addressed in many previous researches however more work needs to be done in terms of effectiveness reliability accuracy as well as usability of the current water quality management methodologies. The goal of this study is to develop a water quality prediction model with the help of water quality factors using Ensemble learning. The evaluation of the accuracy of the applied models according to the error indexes declared that ensemble learning was the most accurate model. Examining the results of the models showed that all of them had some over-estimation properties.
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Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | T.V.Ananda Babu | Sri Venkatesa Perumal college of Engineering & Technology |
| 2 | C.Lohitha | Sri Venkatesa Perumal college of Engineering & Technology |
| 3 | G.Snehalatha | Sri Venkatesa Perumal college of Engineering & Technology |
| 4 | B.Pavan Kumar | Sri Venkatesa Perumal college of Engineering & Technology |
| 5 | C.Yerriswami | Sri Venkatesa Perumal college of Engineering & Technology |
| 6 | G.Santhosh | Sri Venkatesa Perumal college of Engineering & Technology |
| 7 | A.Umesh chandra | Sri Venkatesa Perumal college of Engineering & Technology |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Babu, T.V.Ananda, C.Lohitha, G.Snehalatha, Kumar, B.Pavan, C.Yerriswami, G.Santhosh, & chandra, A.Umesh (2025). Data - driven Explainable Predictive Features For Water Quality Prediction Using Ensemble Learning. International Journal of Advance Research and Innovative Ideas In Education, 11(2), 3066-3071.
MLA Style
Babu, T.V.Ananda, et al. "Data - driven Explainable Predictive Features For Water Quality Prediction Using Ensemble Learning." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, 2025, pp. 3066-3071.
IEEE Style
T.V.Ananda Babu, C.Lohitha, G.Snehalatha, B.Pavan Kumar, C.Yerriswami, G.Santhosh, and A.Umesh chandra, "Data - driven Explainable Predictive Features For Water Quality Prediction Using Ensemble Learning," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 2, pp. 3066-3071, 2025.
Vancouver Style
Babu T.V.Ananda, C.Lohitha, G.Snehalatha, Kumar B.Pavan, C.Yerriswami, G.Santhosh, et al. Data - driven Explainable Predictive Features For Water Quality Prediction Using Ensemble Learning. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(2):3066-3071.
Harvard Style
Babu, T.V.Ananda, C.Lohitha, G.Snehalatha, Kumar, B.Pavan, C.Yerriswami, G.Santhosh, & chandra, A.Umesh (2025) 'Data - driven Explainable Predictive Features For Water Quality Prediction Using Ensemble Learning', International Journal of Advance Research and Innovative Ideas In Education, 11(2), pp. 3066-3071.
Chicago Style
Babu, T.V.Ananda, et al. "Data - driven Explainable Predictive Features For Water Quality Prediction Using Ensemble Learning." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 3066-3071.
Turabian Style
Babu, T.V.Ananda, et al. "Data - driven Explainable Predictive Features For Water Quality Prediction Using Ensemble Learning." International Journal of Advance Research and Innovative Ideas In Education 11, no. 2 (2025): 3066-3071.
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