用大模型让普通人也能轻松操作专业水务仿真工具。
Large Language Models for Water Distribution Systems Modeling and Decision-Making
- 通过自然语言指令自动转化并执行水务模拟任务。
- 复杂任务准确率达56%-81%，简单任务超90%。
- 适合水务工程师和非专业用户快速上手仿真分析。
大型语言模型（LLMs）融入工程工作流，为降低计算工具使用门槛带来新机遇，尤其在因技术或专业壁垒导致工具利用率低的领域，如供水管网系统（WDS）管理。本研究提出 LLM-EPANET 框架，一种基于代理的系统，实现用户通过自然语言与基准仿真工具 EPANET 的交互。该框架结合检索增强生成与多代理协同机制，可自动将用户查询转化为可执行代码，运行仿真并返回结构化结果。研究构建了包含69个基准查询的评估集，用于测试主流LLM的表现。结果显示，LLMs能有效支持多种建模任务，整体准确率为56%-81%，简单查询准确率超过90%。这些发现表明，基于LLM的建模有望通过透明、交互式的AI界面，推动水务领域数据驱动决策的普及。框架代码与基准查询已开源：https://github.com/yinon-gold/LLMs-in-WDS-Modeling。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) into engineering workflows presents new opportunities for making computational tools more accessible. Especially where such tools remain underutilized due to technical or expertise barriers, such as water distribution system (WDS) management. This study introduces LLM-EPANET, an agent-based framework that enables natural language interaction with EPANET, the benchmark WDS simulator. The framework combines retrieval-augmented generation and multi-agent orchestration to automatically translate user queries into executable code, run simulations, and return structured results. A curated set of 69 benchmark queries is introduced to evaluate performance across state-of-the-art LLMs. Results show that LLMs can effectively support a wide range of modeling tasks, achieving 56-81% accuracy overall, and over 90% for simpler queries. These findings highlight the potential of LLM-based modeling to democratize data-driven decision-making in the water sector through transparent, interactive AI interfaces. The framework code and benchmark queries are shared as an open resource: https://github.com/yinon-gold/LLMs-in-WDS-Modeling.
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