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

Abstract

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.