Automating SWMM-based stormwater modelling and analysis through a tool-augmented single-agent system.

Abstract

Urban stormwater modelling plays a critical role in assessing interventions for flood risk and water quality management in response to ageing infrastructure and future uncertainties. However, modelling workflows in practice remain highly manual, and key steps in model configuration, execution, and interpretation often depend on specialised knowledge, leading to inefficiencies. Therefore, this study proposes SWMM-Agentic, a tool-augmented, large language model (LLM)-based single-agent system for urban stormwater modelling, simulation, and scenario analysis. Built on the Storm Water Management Model (SWMM), SWMM-Agentic uses one orchestration model to interpret natural-language instructions and sequentially invoke documented functions for traceable post-configuration workflows. Evaluation on the Astlingen benchmark included capability demonstrations and a 60-task suite comprising 20 static, 20 dynamic, and 20 scenario-based tasks, executed once with each of three LLMs to produce 180 model-task runs. DeepSeek-V3.2-Exp successfully completed 59/60 tasks (98.3%), Qwen3-236B completed 58/60 (96.7%), and Qwen3-14B completed 45/60 (75.0%). Across 180 runs, 89 of 100 failed tool calls were followed by a successful corrective call within three attempts. SWMM-Agentic also reproduced network characteristics, compared alternative control strategies, and conducted a human-framed rain-garden experiment that showed decreasing combined sewer overflow discharge with diminishing marginal benefits at higher coverage. These results demonstrate that SWMM-Agentic can reliably operate existing SWMM models through natural language within the evaluated benchmark and tool scope, supporting accurate and reproducible stormwater simulation and analysis, and laying the groundwork for natural-language-driven platforms for integrated planning and hypothesis-driven research.