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

人工智能框架利用大型语言模型解决约旦的水流失问题

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一个人工智能驱动的框架，通过减少约旦50%的水流失的非收益水（NRW）来应对水资源短缺。该系统集成了水力建模、数字孪生、SCADA数据和大型语言模型代理，以持续监控水网并调整决策。通过Ollama使用llama3.1:8b进行的概念验证演示了自动异常检测和健康报告，响应时间不到两分钟。 AI

影响 该框架可以为水资源匮乏地区提供可扩展的解决方案，利用人工智能提高运营效率和资源管理。

排序理由 该集群包含一篇发表在arXiv上的研究论文，详细介绍了一个新的人工智能驱动的水管理框架。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [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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