Jordan faces severe water scarcity, with 50% of produced water lost to leakage, theft, and metering issues, known as non-revenue water (NRW). Traditional reactive approaches have proven insufficient for sustained NRW reduction. This paper proposes an intelligent framework integrating EPANET hydraulic modeling, digital twin technology, SCADA systems, and LLM-based AI agents for continuous network monitoring and adaptive decision-making. The system combines real-time data streams with physics-based simulation to detect anomalies, employing retrieval-augmented generation (RAG) for policy interpretation and function calling for network control.
A proof-of-concept implementation validates technical feasibility using EPYT with offline LLMs (llama3.1:8b via Ollama) on a 1,164-junction Amman district network. The system demonstrates automated hydraulic simulation, flow-based anomaly detection aligned with water distribution zone (DZ) practice, and AI-generated health reports with response times under 2 minutes and zero API costs. Burst detection relies on local flow anomaly analysis: a 30.1 L/s simulated leak produces measurable flow redistribution in 15 pipes, flagging a 15-junction cluster that localizes the burst, confirming alignment with DZ monitoring practice.
The framework accommodates Jordan's intermittent supply patterns and limited automation through phased implementation, offering a scalable pathway for water-scarce regions to leverage intelligent automation for NRW reduction and operational efficiency.
Blogger's Review: This study showcases the immense potential of AI technology in water resource management, especially in resource-scarce regions. By integrating multiple technological approaches, the framework not only enhances monitoring capabilities of water loss but also provides data-driven support for decision-making, making it a model worth replicating in other areas.