Abstract Submission: 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 (LLM) hold new opportunities 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 will be tested with increasing levels of query complexity to study the ability of LLMs to interact with WDS models, run complex simulations, and provide explanations regarding the model outcomes. The performance of the proposed framework will be evaluated using state-of-the-art LLM accuracy metrics, demonstrating its potential to enhance decision-making processes in WDS management.
