Control-Oriented System Identification of Urban Drainage Dynamics for Flood Mitigation

Urban drainage networks play a critical role in mitigating flood risk in rapidly urbanizing environments, yet their dynamic behavior under rainfall forcing remains difficult to describe using simple mathematical models. Most existing studies rely on detailed hydrodynamic simulators, such as EPA-SWMM, which are well suited for design and scenario analysis but are less suitable to system-level analysis and control-oriented modeling due to their complexity.
This study proposes a control-oriented modeling framework for urban drainage systems based on system identification techniques applied to SWMM-generated input-output data. Rainfall-runoff and hydraulic responses are simulated for an urban drainage network under multiple storm events. Time series of inflow and water depth are extracted at selected junctions identified as critical using flooding and surcharge indicators.
A physics-based mass balance formulation is adopted, and local storage-outflow dynamics are linearized around representative operating conditions. Using the resulting input-output data, first-order dynamic models are identified for individual drainage nodes. The derived transfer functions capture the dominant inflow-depth dynamics, where the static gain represents the steady-state sensitivity of water depth to inflow variations and the time constant reflects the effective storage behavior of the drainage node.
Model performance is evaluated by testing the identified models under different rainfall events. The proposed approach provides compact and interpretable models that bridge detailed hydraulic simulation and control-oriented analysis, supporting digital water and digital twin applications for urban drainage systems.