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

2026-06-14

AI Summary

Jordan faces severe water scarcity, with non-revenue water (NRW) reaching as high as 50%, a challenge that conventional management approaches struggle to mitigate effectively. This study proposes an adaptive water distribution network management framework integrating EPANET hydraulic modeling, digital twin technology, SCADA systems, and a locally deployed large language model (Llama3.1-8B via Ollama). For the first time, the framework combines retrieval-augmented generation (RAG)-driven AI agents with hydraulic simulation to enable policy interpretation, function calling, and autonomous, zero-API-cost decision-making under intermittent supply conditions. Validated on Amman’s 1,164-node network, the system generates comprehensive health reports within two minutes and accurately localizes a 30.1 L/s leak to a cluster of 15 nodes, aligning closely with district metered area monitoring practices.

Abstract

Jordan faces severe water scarcity with 50\% of water produced is lost to leakage, theft and metering issues also 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 large language model (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 localises the burst -- confirming alignment with water distribution zone (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.

Problem

• Non-Revenue Water
• water scarcity
• leakage detection
• water loss management
• intermittent water supply

Innovation

• AI-driven water management
• digital twin
• large language model (LLM)
• non-revenue water (NRW)
• retrieval-augmented generation (RAG)