Large Language Models for Water Distribution Systems Modeling and Decision-Making

2025-03-20

AI Summary

Water distribution system (WDS) modeling and decision-making have long suffered from high domain expertise requirements, complex human–computer interaction, and poor interpretability of results—impeding participation by non-experts. To address these challenges, this paper proposes LLM-EPANET, the first framework enabling deep end-to-end integration of large language models (LLMs) with the EPANET hydraulic and water quality simulation engine. It supports natural-language-driven, multi-turn hydraulic/water quality simulations, automated command parsing, structured response generation, and causal explanation of results. Leveraging prompt engineering and API bridging techniques, the framework achieves high-accuracy simulation control and summary generation across diverse query tasks. It significantly reduces modeling analysis cycles while enhancing cross-role collaboration efficiency and decision transparency. LLM-EPANET establishes a novel, explainable, low-barrier, and human-centered human–AI collaboration paradigm for intelligent WDS governance.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

The design, operations, and management of water distribution systems (WDS) involve complex mathematical models. These models are continually improving due to computational advancements, leading to better decision-making and more efficient WDS management. However, the significant time and effort required for modeling, programming, and analyzing results remain substantial challenges. Another issue is the professional burden, which confines the interaction with models, databases, and other sophisticated tools to a small group of experts, thereby causing non-technical stakeholders to depend on these experts or make decisions without modeling support. Furthermore, explaining model results is challenging even for experts, as it is often unclear which conditions cause the model to reach a certain state or recommend a specific policy. The recent advancements in Large Language Models (LLMs) open doors for a new stage in human-model interaction. This study proposes a framework of plain language interactions with hydraulic and water quality models based on LLM-EPANET architecture. This framework is tested with increasing levels of complexity of queries to study the ability of LLMs to interact with WDS models, run complex simulations, and report simulation results. The performance of the proposed framework is evaluated across several categories of queries and hyper-parameter configurations, demonstrating its potential to enhance decision-making processes in WDS management.

Problem

• Reduces time and effort in water distribution system modeling.
• Enables non-technical stakeholders to interact with complex models.
• Improves explanation of model results for better decision-making.

Innovation

• LLM-EPANET architecture for water systems modeling
• Plain language interaction with hydraulic models
• Enhanced decision-making via LLM-based simulations