AI-driven multi-agent digital twin conceptual framework for real-time domestic water quality monitoring, purification and smart billing

Access to safe drinking water remains a critical global challenge, with over 2.2 billion people lacking reliable access despite water’s abundance, as only 3% of Earth’s water is freshwater. Current systems suffer from unreliable analog metering, tampering risks, and the absence of real-time platforms for monitoring quality, consumption, purification and billing—creating a literature gap in scalable, integrated, and intelligent water management solutions. To address this situation, a pilot-scale, machine learning–enabled multi-agent digital twin-inspired framework is developed for domestic water systems, integrating IoT sensors to continuously monitor key parameters (pH, turbidity, temperature, flow, and TDS); purification status; and employing predictive models for anomaly detection and forecasting. Among several tested algorithms (ARIMA, autoregression, and additive regression), additive regression demonstrated comparatively better performance under the constraints of the pilot dataset, relatively low error rates were achieved under evaluation conditions for temperature, pH, flow, pressure, TDS and color. The system links to a web-based platform providing real-time visualization, automated billing, and user alerts, while incorporating an integrated purification module capable of reducing heavy metals and microbial load with above 96.96% accuracy. These actions result in a compact end-to-end prototype framework that demonstrates the feasibility of safe real-time water use under controlled conditions while improving accountability, reducing billing errors, and promoting conservation through predictive alerts. The novelty lies in the seamless integration of IoT sensing, ML-driven analytics, purification, and digital billing into a pilot-scale integrated digital twin framework. This system offers future prospects for deployment in smart city infrastructures, enhanced resilience through cybersecurity integration, and alignment with SDG 6 (Clean Water and Sanitation), indicating its potential as a future step toward scalable water management solutions. However, the system is validated under limited pilot-scale deployment conditions, and further large-scale validation is required.