MACHINE LEARNING-BASED PREDICTION OF WATER QUALITY USING PHYSICOCHEMICAL AND ENVIRONMENTAL INDICATORS
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Abstract
Reliable prediction of water quality can strengthen environmental monitoring by integrating conventional measurements with data-driven analytical approaches. This study evaluated the capacity of physicochemical, environmental, spatial, and hydrological indicators to predict an integrated water-quality score using machine-learning regression. The analysis included 36 complete observations from Anuppur, Dindori, and Jabalpur and incorporated indicators of oxygen conditions, organic loading, nutrients, ionic composition, microbiological contamination, rainfall, water level, season, and location. Four algorithms—Ridge Regression, Random Forest, Gradient Boosting, and Decision Tree Regression—were evaluated using six-fold cross-validation. Distinct spatial patterns were observed, with Jabalpur recording the highest mean water-quality impairment score and Anuppur the lowest. Total coliform, phosphate, BOD, conductivity, dissolved solids, and COD showed strong positive associations with the integrated outcome. Ridge Regression achieved the strongest overall predictive performance (R² = 0.603; RMSE = 6.98; MAE = 5.01), while Random Forest produced the lowest MAE (4.93). The findings demonstrate that a parsimonious machine-learning framework can capture meaningful water-quality variability from multidimensional environmental measurements. Although the limited sample size constrains generalizability, the approach provides a practical basis for identifying influential indicators and supporting targeted water-quality monitoring.
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References
2. Ahmed, U., Mumtaz, R., Anwar, H., Mumtaz, S., & Qamar, A. M. (2020). Water quality monitoring: from conventional to emerging technologies. Water Supply, 20(1), 28-45.
3. Akhtar, N., Syakir Ishak, M. I., Bhawani, S. A., & Umar, K. (2021). Various natural and anthropogenic factors responsible for water quality degradation: A review. Water, 13(19), 2660.
4. Alwan, I. A., & Saeed, I. O. (2024). Monitoring Pollution Indicators of the Water of the Tigris River in Tikrit and its Suburbs. Egyptian Journal of Aquatic Biology & Fisheries, 28(2), 131.
5. Armstrong, A., Dyer, E., Koehler, J., & Hope, R. (2022). Intra-seasonal rainfall and piped water revenue variability in rural Africa. Global Environmental Change, 76, 102592.
6. Babatunde, A. A. (2024). A study on traditional water quality assessment methods. International Journal of Economics, Business and Social Science Research, 2(3), 62-75.
7. Bănăduc, D., Simić, V., Cianfaglione, K., Barinova, S., Afanasyev, S., Öktener, A., ... & Curtean-Bănăduc, A. (2022). Freshwater as a sustainable resource and generator of secondary resources in the 21st century: Stressors, threats, risks, management and protection strategies, and conservation approaches. International journal of environmental research and public health, 19(24), 16570.
8. Deng, Y., Zhang, Y., Pan, D., Yang, S. X., & Gharabaghi, B. (2024). Review of recent advances in remote sensing and machine learning methods for lake water quality management. Remote Sensing, 16(22), 4196.
9. Dilip, M. (2024). Narmatha water quality [Data set]. Kaggle. https://www.kaggle.com/datasets/ajithdari/narmatha-water-quality
10. Elguoshy, A., Zedan, H., & Saito, S. (2025). Machine learning-driven insights in cancer metabolomics: from subtyping to biomarker discovery and prognostic modeling. Metabolites, 15(8), 514.
11. Huang, Y., Wang, X., Xiang, W., Wang, T., Otis, C., Sarge, L., ... & Li, B. (2022). Forward-looking roadmaps for long-term continuous water quality monitoring: bottlenecks, innovations, and prospects in a critical review. Environmental science & technology, 56(9), 5334-5354.
12. Koppanen, M., Kesti, T., Kokko, M., Rintala, J., & Palmroth, M. (2022). An online flow-imaging particle counter and conventional water quality sensors detect drinking water contamination in the presence of normal water quality fluctuations. Water research, 213, 118149.
13. Lencha, S. M., Ulsido, M. D., & Muluneh, A. (2021). Evaluation of seasonal and spatial variations in water quality and identification of potential sources of pollution using multivariate statistical techniques for Lake Hawassa Watershed, Ethiopia. Applied Sciences, 11(19), 8991.
14. Mishra, R. K. (2023). Fresh water availability and its global challenge. British journal of multidisciplinary and advanced studies, 4(3), 1-78.
15. Misman, N. A., Sharif, M. F., Chowdhury, A. J. K., & Azizan, N. H. (2023). Water pollution and the assessment of water quality parameters: a review. Desalination and water treatment, 294, 79-88.
16. Nayar, R. (2020). Assessment of water quality index and monitoring of pollutants by physico-chemical analysis in water bodies: a review. International Journal of Engineering Research and Technology, 9(01), 178-185.
17. Paepae, T., Bokoro, P. N., & Kyamakya, K. (2021). From fully physical to virtual sensing for water quality assessment: A comprehensive review of the relevant state-of-the-art. Sensors, 21(21), 6971.
18. Ren, X., Mi, Z., & Georgopoulos, P. G. (2020). Comparison of Machine Learning and Land Use Regression for fine scale spatiotemporal estimation of ambient air pollution: Modeling ozone concentrations across the contiguous United States. Environment international, 142, 105827.
19. Umwali, E. D., Kurban, A., Isabwe, A., Mind’je, R., Azadi, H., Guo, Z., ... & Sabirhazi, G. (2021). Spatio-seasonal variation of water quality influenced by land use and land cover in Lake Muhazi. Scientific Reports, 11(1), 17376.
20. Yu, Q., Ji, W., Prihodko, L., Ross, C. W., Anchang, J. Y., & Hanan, N. P. (2021). Study becomes insight: Ecological learning from machine learning. Methods in Ecology and Evolution, 12(11), 2117-2128.
21. Zhao, C. S., Yang, Y., Yang, S. T., Xiang, H., Ge, Y. R., Zhang, Z. S., ... & Yu, Q. (2020). Effects of spatial variation in water quality and hydrological factors on environmental flows. Science of the Total Environment, 728, 138695.
22. Zhong, S., Zhang, K., Bagheri, M., Burken, J. G., Gu, A., Li, B., ... & Zhang, H. (2021). Machine learning: new ideas and tools in environmental science and engineering. Environmental science & technology, 55(19), 12741-12754.
23. Zhu, D., Cheng, X., Li, W., Niu, F., & Wen, J. (2022). Temporal and spatial variation characteristics of water quality in the middle and lower reaches of the Lijiang River, China and their responses to environmental factors. International Journal of Environmental Research and Public Health, 19(13), 8089.