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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Leveraging large language models for automating water distribution network optimization.

Jian Wang1, Guangtao Fu1, Dragan Savic2

  • 1Centre for Water Systems, University of Exeter, Exeter EX4 4QF, UK.

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|September 13, 2025
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Summary
This summary is machine-generated.

Large Language Model (LLM) agents can automate water distribution network management. A novel framework shows LLM agents can perform hydraulic tasks, with coding agents offering the most accurate results for optimization.

Keywords:
AI agentDeepSeekHydraulic optimizationLarge language modelWater distribution network

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Area of Science:

  • Hydraulic Engineering
  • Artificial Intelligence
  • Water Resource Management

Background:

  • Effective management of Water Distribution Networks (WDNs) is crucial for urban water supply.
  • Current WDN management relies on complex modeling and optimization, requiring specialized expertise.
  • Advancements in Large Language Models (LLMs) present opportunities for automating these tasks.

Purpose of the Study:

  • To present and evaluate an LLM-based agent framework for automating WDN management.
  • To assess the feasibility and limitations of LLM agents in hydraulic model calibration and pump operation optimization.

Main Methods:

  • Developed an LLM agent framework with an Orchestrating Agent and three specialized agents: Knowledge, Modelling (EPANET), and Coding.
  • Tested the framework on two benchmark WDNs (Net2 and Anytown) for hydraulic model calibration and pump operation optimization.
  • Evaluated agent performance based on reasoning, reliability, and accuracy in executing hydraulic tasks.

Main Results:

  • The Knowledge Agent replicated expert hydraulic reasoning but lacked numerical precision.
  • The Modelling Agent improved reliability but struggled with natural language numerical constraints, especially in looped networks.
  • The Coding Agent demonstrated consistent and accurate performance in iterative code generation and execution for optimization.

Conclusions:

  • LLM-based agents show significant potential for automated and accurate hydraulic optimization in WDNs.
  • The developed framework represents a step towards LLM-driven multi-agent systems for hydraulic decision-making.
  • Future work can focus on specialized LLM applications for complex hydraulic management scenarios.