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. 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
The increasing severity of water scarcity globally and advancements in AI and digital twin technologies are converging, making such integrated solutions feasible and necessary.
This development demonstrates a practical application of AI and digital twins to address critical resource management, moving beyond theoretical discussions to implementable solutions that have direct economic and societal impacts.
The shift from reactive to proactive and adaptive water management, enabled by real-time data and AI-driven decision-making, could fundamentally change how utilities operate and manage scarce resources.
- · Water utilities
- · Smart city technology providers
- · AI companies specialized in resource management
- · Countries facing water scarcity
- · Traditional water infrastructure companies
- · Regions heavily reliant on outdated water management practices
Reduced non-revenue water and improved water security in regions adopting similar AI-driven systems.
Increased investment in digital infrastructure and AI capabilities within critical utilities worldwide, leading to new market opportunities.
The establishment of global standards and best practices for AI-augmented national infrastructure, potentially driving a new wave of tech diplomacy.
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Read at arXiv cs.AI