AI-Driven Water Quality Monitoring and Predictive Bioremediation Framework for Urban Wastewater Systems: An Integrated IoT and Machine Learning Approach

Rapid urbanization across Indian cities has placed severe strain on existing wastewater management infrastructure, driving widespread contamination of surface and groundwater bodies. Conventional monitoring approaches depend on periodic laboratory analyses that fail to capture the dynamic spatiotemporal variability inherent in complex urban wastewater systems. This study proposes an integrated framework combining Internet of Things (IoT)-based real-time sensor networks with Machine Learning (ML) predictive models and AI-guided bioremediation protocols for comprehensive urban wastewater quality management. A multi-parameter sensor array measuring pH, dissolved oxygen, biochemical oxygen demand (BOD), chemical oxygen demand (COD), turbidity, nitrate, phosphate, heavy metal concentration, and temperature at sub-hourly intervals was deployed across seven nodes in a peri-urban Chennai catchment. A hybrid deep learning architecture (HydraSenze-AI v2.0) combining Long Short-Term Memory (LSTM) networks with Random Forest classifiers achieved a cross-validated contamination event prediction accuracy of 91.3% F1-score at 24-hour horizons. Prediction outputs dynamically scheduled bioremediation interventions using optimized consortia of Bacillus subtilis, Pseudomonas putida, and Rhodotorula mucilaginosa tailored to detected pollutant profiles. Pilot deployment in a peri-urban Chennai catchment demonstrated a 67% reduction in BOD load, 58% reduction in heavy metal concentration, a 33-percentage-point improvement in CPCB Class-C compliance, and a 34% freshwater substitution potential. These results demonstrate significant promise for scalable, cost-effective, AI-enabled urban water sustainability aligned with India's National Water Mission and SDG-6 targets.

Artificial Intelligence, Bioremediation, IoT Sensors, LSTM Networks, Machine Learning, Urban Wastewater, Water Quality Monitoring

1. Hamid, S., Ul Islam, S., Ahmad, S., & Bilal, M. (2020). LoRaWAN-based IoT framework for distributed water quality monitoring in urban catchments. Sensors, 20(4), 1135. [Google Scholar] [Crossref]

2. Hameed, M., Sharqi, S. S., Yaseen, Z. M., Afan, H. A., Hussain, A., & Elshafie, A. (2017). Application of artificial intelligence (AI) techniques in water quality index prediction. Water and Environment Journal, 31(4), 555–578. [Google Scholar] [Crossref]

3. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. [Google Scholar] [Crossref]

4. Hu, Z., Zhang, Y., Zhao, Y., Xie, M., Zhong, J., Tu, Z., & Liu, J. (2019). A water quality prediction method based on the deep LSTM network considering correlation in smart mariculture. Sensors, 19(6), 1420. [Google Scholar] [Crossref]

5. Jha, M. K., Sahoo, S., & Ghosh, T. (2022). IoT-enabled real-time water quality monitoring in Ganga tributary systems. Environmental Monitoring and Assessment, 194(8), 592. [Google Scholar] [Crossref]

6. Kumar, A., Singh, R., & Joshi, H. (2021). Synergistic bioremediation efficiency of multi-species microbial consortia for simulated urban wastewater treatment. Bioresource Technology, 337, 125438. [Google Scholar] [Crossref]

7. Ministry of Jal Shakti (2022). National Water Policy 2022 (Draft). Government of India, Department of Water Resources, New Delhi. [Google Scholar] [Crossref]

8. Mukherjee, P., Roy, S., & Datta, S. (2020). Cross-feeding interactions in Bacillus subtilis and Pseudomonas putida consortia and heavy metal remediation. Environmental Science & Technology, 54(15), 9283–9292. [Google Scholar] [Crossref]

9. NITI Aayog (2021). Composite Water Management Index 2021. Government of India, New Delhi. [Google Scholar] [Crossref]

10. Rasin, Z., & Abdullah, M. R. (2009). Water quality monitoring system using ZigBee-based wireless sensor network. International Journal of Engineering and Technology, 9(10), 14–18. [Google Scholar] [Crossref]

11. Zhang, Y., Li, X., Zhang, H., & Liu, W. (2023). Hybrid CNN-LSTM model for spatiotemporal water quality prediction across distributed sensor networks. Journal of Hydrology, 620, 129451. [Google Scholar] [Crossref]

12. Central Pollution Control Board (2021). Water Quality Standards for Discharge of Environmental Pollutants. CPCB, Ministry of Environment, Forest and Climate Change, New Delhi. [Google Scholar] [Crossref]

13. Patel, D., Chaturvedi, R. K., & Goswami, A. (2023). AI-driven wastewater treatment optimization in Indian smart cities. Clean Technologies and Environmental Policy, 25, 1847–1863. [Google Scholar] [Crossref]

14. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. [Google Scholar] [Crossref]

15. Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350–361. [Google Scholar] [Crossref]

16. Alharbi, S. A., Wainwright, M., Alhussain, M. S., Saleh, M. S., Al-Jabr, A. M., & Crowley, D. E. (2013). Description of new Bacillus subtilis isolates that degrade poly-lactic acid. Bioscience, Biotechnology and Biochemistry, 77(6), 1454–1458. [Google Scholar] [Crossref]

17. Govarthanan, M., Mythili, R., Selvankumar, T., Kamala-Kannan, S., & Kim, H. (2018). Bioremediation of mercury and lead contaminated soil using Pseudomonas sp. Environmental Science and Pollution Research, 25(33), 33024–33034. [Google Scholar] [Crossref]

18. World Bank Group (2023). India Water Security: Challenges and Solutions (Report No. ACS26101). Washington, D.C. [Google Scholar] [Crossref]

19. MoEFCC (2022). National Wetland Conservation Programme Status Report 2021–22. Ministry of Environment, Forest and Climate Change, Government of India. [Google Scholar] [Crossref]

20. Memon, F. A., Zhong, Z., Butler, D., Shirley-Smith, C., Lui, S., Makropoulos, C., & Avery, L. (2007). Integrated resource recovery from wastewater: A case study in wastewater engineering. Journal of Environmental Planning and Management, 50(2), 263–277. [Google Scholar] [Crossref]

21. Bureau of Indian Standards (2012). IS 10500:2012 — Drinking Water Specification (Second Revision). BIS, New Delhi. [Google Scholar] [Crossref]

22. United Nations (2023). Sustainable Development Goals Report 2023 — SDG-6: Clean Water and Sanitation. UN DESA, New York. [Google Scholar] [Crossref]

23. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press, Cambridge, MA. [Google Scholar] [Crossref]

24. Raikwar, V., Kumar, P., Raikwar, D., & Kumar, M. (2019). Contamination of water resources by pharmaceutical pollutants and treatment using constructed wetlands. Environmental Chemistry Letters, 17(1), 309–329. [Google Scholar] [Crossref]

25. Borse, V., Bhatt, D., Parekh, F., & Sali, A. (2022). Advancement of Rhodotorula sp. in bioremediation of heavy metals and synthetic dyes from industrial wastewater. Environmental Research, 212(Part B), 113286. [Google Scholar] [Crossref]

26. Box, G. E. P., & Behnken, D. W. (1960). Some new three-level designs for the study of quantitative variables. Technometrics, 2(4), 455–475. [Google Scholar] [Crossref]

27. Smart Cities Mission (2023). Smart Cities Mission Annual Report 2022–23. Ministry of Housing and Urban Affairs, Government of India, New Delhi. [Google Scholar] [Crossref]

28. Liaw, A., & Wiener, M. (2002). Classification and regression by Random Forest. R News, 2(3), 18–22. [Google Scholar] [Crossref]

29. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD, 785–794. [Google Scholar] [Crossref]

30. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958. [Google Scholar] [Crossref]

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