AI Framework Boosts Water Network Management, Reduces Losses in Jordan

Researchers have proposed an intelligent framework designed to enhance adaptive water network management, with a particular focus on reducing non-revenue water (NRW) losses in regions like Jordan, which faces severe water scarcity. This framework combines several advanced technologies: EPANET hydraulic modeling, digital twin technology, SCADA systems for real-time data, and large language model (LLM)-based AI agents for dynamic decision-making. The system is engineered to integrate real-time data streams with physics-based simulations to identify anomalies, using retrieval-augmented generation (RAG) for policy interpretation and function calling for network control. A proof-of-concept implementation validated the technical feasibility using offline LLMs (Llama 3.1:8b via Ollama) on a 1,164-junction network in Amman. The system successfully demonstrated automated hydraulic simulation, flow-based anomaly detection consistent with water distribution zone practices, and the generation of AI-powered health reports within two minutes, incurring zero API costs. For instance, a simulated leak of 30.1 L/s was accurately localized to a 15-junction cluster through flow redistribution analysis. The framework is designed to accommodate intermittent supply patterns and limited automation, offering a scalable solution for water-scarce regions to leverage intelligent automation for NRW reduction and operational efficiency.