AI-Driven Framework for Adaptive Water Network Management with Proof-of-Concept Implementation: Addressing Non-Revenue Water in Jordan

2026-06-14 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Water Management Systems · Depth: Expert, quick

Summary

Jordan, a water-scarce nation, faces 50% non-revenue water (NRW) losses. A new intelligent framework integrates EPANET hydraulic modeling, digital twin technology, SCADA systems, and LLM-based AI agents for continuous water network monitoring and adaptive decision-making. This system combines real-time data with physics-based simulation to detect anomalies, using retrieval-augmented generation (RAG) for policy interpretation and function calling for network control. A proof-of-concept, implemented with EPYT and offline llama3.1:8b LLMs via Ollama on a 1,164-junction Amman district network, validated its technical feasibility. It demonstrated automated hydraulic simulation, flow-based anomaly detection aligned with water distribution zone (DZ) practice, and AI-generated health reports with sub-2-minute response times and zero API costs. A simulated 30.1 L/s leak was localized to a 15-junction cluster, confirming practical alignment. The framework supports intermittent supply and limited automation, offering a scalable solution for NRW reduction in water-scarce regions.

Key takeaway

For AI Architects or Research Scientists developing smart water infrastructure, this framework offers a robust approach to combat non-revenue water. You should evaluate integrating offline LLM-based AI agents with existing hydraulic models and SCADA systems to achieve real-time anomaly detection and adaptive network control. Consider its phased implementation strategy to accommodate intermittent supply and limited automation, providing a scalable, cost-effective solution for improving operational efficiency in water-scarce regions.

Key insights

An AI-driven framework integrates hydraulic modeling and real-time data for adaptive water network management and non-revenue water reduction.

Principles

• Combine physics-based simulation with real-time data.
• Utilize offline LLMs for cost-effective AI agents.
• Align anomaly detection with operational zone practices.

Method

The framework integrates EPANET, digital twin, SCADA, and LLM-based AI agents to monitor networks, detect anomalies via flow analysis, interpret policies using RAG, and control systems via function calling.

In practice

• Implement offline LLMs like llama3.1:8b via Ollama.
• Use EPYT for hydraulic modeling integration.
• Develop flow-based anomaly detection for water distribution zones.

Topics

• Water Network Management
• Non-Revenue Water
• AI Agents
• Digital Twin Technology
• Hydraulic Modeling
• SCADA Systems

Best for: AI Scientist, AI Engineer, Research Scientist, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.