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[2503.16191] Large Language Models for Water Distribution Systems Modeling and Decision-Making
Summary
This article discusses the integration of Large Language Models (LLMs) into water distribution system management, introducing LLM-EPANET, a framework that enables natural language interaction with EPANET, enhancing accessibility and decision-making.
Why It Matters
The study highlights the potential of LLMs to democratize access to advanced modeling tools in water management, addressing technical barriers and improving decision-making processes in the sector. This is particularly relevant as water resources face increasing challenges globally.
Key Takeaways
- LLM-EPANET allows users to interact with water distribution models using natural language.
- The framework demonstrates a significant accuracy rate of 56-81% for various modeling tasks.
- Over 90% accuracy was achieved for simpler queries, showcasing the effectiveness of LLMs.
- The study provides a curated set of benchmark queries for evaluating LLM performance.
- This research aims to make data-driven decision-making more accessible in the water sector.
Computer Science > Artificial Intelligence arXiv:2503.16191 (cs) [Submitted on 20 Mar 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:Large Language Models for Water Distribution Systems Modeling and Decision-Making Authors:Yinon Goldshtein, Gal Perelman, Assaf Schuster, Avi Ostfeld View a PDF of the paper titled Large Language Models for Water Distribution Systems Modeling and Decision-Making, by Yinon Goldshtein and 3 other authors View PDF Abstract: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, ...