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- Vol. 5 Issue 3
- Article
Automated Water Quality Index Classification via Gradient Boosting: Identifying BOD, Fecal Coliform and DO as Key Predictors
Authors
1 2 Department of Computer Science, Hemchandracharya North Gujarat University, Gujarat, India. 3 I.T. Sheliya Jafari BCA College, Hemchandracharya North Gujarat University, Gujarat, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 859-867
- Article Type
- Original Article
References
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Water Supply, vol. 20, no. 1, pp. 28-45, 2020, doi: 10.2166/ws.2019.144.
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Delta," J. Hydrol., vol. 631, p. 130813, 2024.
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San Francisco, CA, USA, 2016, pp. 785-794.
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Process, vol. 5, no. 2, pp. 1-11, 2015.
24. T. Fawcett, "An introduction to ROC analysis," Pattern Recognit. Lett., vol. 27, no. 8, pp. 861-874, 2006.
25. M. Zhu, W. Wang, J. Yang, and Z. Zhang, "Statistical characterization of physicochemical water quality parameters for distribution analysis
in river monitoring," Environ. Sci. Pollut. Res., vol. 30, no. 5, pp. 11284-11299, 2023.
26. R. Chen, M. Liu, and Y. Zhang, "Regularisation strategies in gradient boosting for imbalanced environmental datasets," Ecol. Inform., vol.
74, p. 101994, 2023.
27. A. Sarkar, P. Roy, and S. Datta, "Policy frameworks for machine learning-based water quality monitoring in South Asian river systems,"
Water Policy, vol. 26, no. 3, pp. 412-429, 2024.
28. I. Osman, A. Fawzy, P. S. Gallagher, and D. W. Rooney, "Generalisation of machine learning models for water quality prediction across
geographically diverse river basins," Environ. Sci. Technol., vol. 55, no. 12, pp. 7898-7910, 2021.
Press, 2017.
2. Singh and S. Panda, "Water Quality Index computation: A comprehensive review," Int. J. Environ. Sci. Technol., vol. 18, no. 4, pp. 993-
1008, 2021.
3. T. Rajaee, S. Khani, and M. Ravansalar, "Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A
review," Chemom.Intell. Lab. Syst., vol. 200, Art. no. 103978, 2020, doi: 10.1016/j.chemolab.2020.103978.
4. M. A. Islam, M. R. Karim, and M. S. Hossain, "Assessment of drinking water quality using WQI and its impact on human health," Groundw.
Sustain. Dev., vol. 10, p. 100350, 2020.
5. Garg, A. Misra, and V. Jain, "Application of machine learning techniques in water quality prediction," Environ. Monit. Assess., vol. 194,
no. 5, p. 344, 2022.
6. S. B. Pawar and A. Pandit, "Predicting water quality parameters using ML for Bhima river basin, India," Environ. Dev. Sustain., vol. 24,
no. 7, pp. 9097-9117, 2022.
7. R. Tripathi, S. Kumar, and P. Sharma, "Ensemble learning for water quality classification in Indian rivers," Comput. Electron. Agric., vol.192, p. 106583, 2022.
8. M. G. Uddin, S. Nash, and A. I. Olbert, "A review of water quality index models and their use for assessing surface water quality," Ecol.
Indic., vol. 122, Art. no. 107218, 2021, doi: 10.1016/j.ecolind.2020.107218.
9. Hafeez, M. Hussain, and N. Akhtar, "Comparison of machine learning models for water quality index prediction," Water, vol. 14, no. 3, p.
418, 2022.
10. J. H. Friedman, "Greedy function approximation: A gradient boosting machine," Ann. Stat., vol. 29, no. 5, pp. 1189-1232, 2001.
11. L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5-32, 2001.
12. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, 1995.
13. Pedregosa et al., "Scikit-learn: Machine learning in Python," J. Mach. Learn. Res., vol. 12, pp. 2825-2830, 2011.
14. M. Najafzadeh, A. Ghaemi, and S. Emamgholizadeh, "Prediction of water quality parameters using evolutionary computing-based
formulations," Int. J. Environ. Sci. Technol., vol. 16, no. 10, pp. 6377-6396, 2019, doi: 10.1007/s13762-018-2049-4.
15. U. Ahmed, R. Mumtaz, H. Anwar, S. Mumtaz, and A. M. Qamar, "Water quality monitoring: From conventional to emerging technologies,"
Water Supply, vol. 20, no. 1, pp. 28-45, 2020, doi: 10.2166/ws.2019.144.
16. L. Pan, B. Zheng, Z. He, D. Cao, Y. Liu, and J. Li, "XGBoost-based prediction model for water quality classification in the Yangtze River
Delta," J. Hydrol., vol. 631, p. 130813, 2024.
17. T. Bui, T. A. Tran, Q. T. Bui, and N. D. Hoang, "Spatial prediction of water quality for classification-based ML: A review," Sci. Total
Environ., vol. 892, p. 164689, 2023.
18. Central Pollution Control Board (CPCB), Water Quality Status of Major Rivers in India, Annual Report, New Delhi, India: Ministry of
Environment, Forest and Climate Change, 2020.
19. Bureau of Indian Standards (BIS), Drinking Water-Specification (Second Revision), IS 10500:2012, New Delhi, India: BIS, 2012.
20. C. Abdi, T. Caleb, and S. Meseret, "Prediction of water quality using machine learning approaches: A review," J. Water Process Eng., vol.
45, p. 102561, 2022.
21. T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min.,
San Francisco, CA, USA, 2016, pp. 785-794.
22. M. Belgiu and L. Drăguţ, "Random forest in remote sensing: A review of applications and future directions," ISPRS J. Photogramm. Remote
Sens., vol. 114, pp. 24-31, 2016.
23. M. Hossin and M. N. Sulaiman, "A review on evaluation metrics for data classification evaluations," Int. J. Data Min. Knowl. Manage.
Process, vol. 5, no. 2, pp. 1-11, 2015.
24. T. Fawcett, "An introduction to ROC analysis," Pattern Recognit. Lett., vol. 27, no. 8, pp. 861-874, 2006.
25. M. Zhu, W. Wang, J. Yang, and Z. Zhang, "Statistical characterization of physicochemical water quality parameters for distribution analysis
in river monitoring," Environ. Sci. Pollut. Res., vol. 30, no. 5, pp. 11284-11299, 2023.
26. R. Chen, M. Liu, and Y. Zhang, "Regularisation strategies in gradient boosting for imbalanced environmental datasets," Ecol. Inform., vol.
74, p. 101994, 2023.
27. A. Sarkar, P. Roy, and S. Datta, "Policy frameworks for machine learning-based water quality monitoring in South Asian river systems,"
Water Policy, vol. 26, no. 3, pp. 412-429, 2024.
28. I. Osman, A. Fawzy, P. S. Gallagher, and D. W. Rooney, "Generalisation of machine learning models for water quality prediction across
geographically diverse river basins," Environ. Sci. Technol., vol. 55, no. 12, pp. 7898-7910, 2021.
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