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AI framework tackles water loss in Jordan using LLMs

By PulseAugur Editorial · [2 sources] ·

Researchers have developed an AI-driven framework to combat water scarcity in Jordan by reducing non-revenue water (NRW), which accounts for 50% of water loss. The system integrates hydraulic modeling, digital twins, SCADA data, and LLM agents to continuously monitor water networks and adapt decision-making. A proof-of-concept using llama3.1:8b via Ollama demonstrated automated anomaly detection and health reporting, with response times under two minutes. AI

IMPACT This framework could offer a scalable solution for water-scarce regions to leverage AI for operational efficiency and resource management.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new AI-driven framework for water management.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Mohammed Fasha, Nahel Al-Maayta, Bilal Sowan, Mohammad Athamneh, Husam Barham ·

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

    arXiv:2606.15709v1 Announce Type: new 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…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Husam Barham ·

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

    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 i…

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