Leveraging large language models for automating water distribution network optimization

Effective management of Water Distribution Networks (WDNs) is essential to ensure efficient and reliable water supply in cities. However, many management tasks require complex system modelling and optimization approaches, which heavily rely on specialized domain expertise and human resources. Recent advancements in Large Language Models (LLMs) offer promising opportunities to automate complex hydraulic decision-making tasks. This study presents an LLM-based agent framework to automate WDN management tasks. Two tasks are considered to evaluate the feasibility and limitations of LLM agents: hydraulic model calibration and pump operation optimization. The key component of the proposed framework is an Orchestrating Agent that interprets tasks and system states, generates update strategies or executable code, and interacts with three specialized agents to carry out implementation: a Knowledge Agent performing reasoning based on hydraulic principles, a Modelling Agent that interfaces with hydraulic simulation tool EPANET, and a Coding Agent that executes code and returns output feedback. To assess the capabilities of these agents, the framework was systematically tested on two benchmark WDNs - Net2 and Anytown. The results indicate that the reasoning capability demonstrated through interaction with the Knowledge Agent effectively replicates expert-level hydraulic thinking, though it lacks numerical precision. In contrast, the Modelling Agent, which integrates external simulation tools, enhances reliability, although interpreting and enforcing numerical constraints expressed in natural language remain challenging, particularly in looped networks such as Anytown where the agent often converged to suboptimal solutions. Furthermore, the Coding Agent, where code for optimization algorithms is iteratively generated and executed, delivers the most consistent and accurate performance across both networks, underscoring its practical potential. These findings highlight the potential of LLM-based agents for automated, accurate hydraulic optimization, and represent a significant step toward LLM-driven multi-agent frameworks for hydraulic decision-making. This work establishes a foundation for future advancements in specialized, domain-focused LLM applications in complex hydraulic management scenarios.