PCA-guided Automatic Calibration of SWMM with Inverse LightGBM and Gaussian Process-based Bayesian Optimization

Rapid urbanization and the intensification of extreme rainfall have increased urban flood risk, thereby strengthening the demand for hydrological modeling to support the design and operation of urban drainage systems. The U.S. EPA Storm Water Management Model (SWMM) has been widely adopted for urban rainfall–runoff simulation. However, reliable application has been hindered by the need to calibrate numerous site-specific parameters that represent catchment and drainage-network characteristics. Trial-and-error calibration has been time-consuming and difficult to reproduce, whereas evolutionary algorithm-based auto calibration has often required thousands of model evaluations and can be computationally prohibitive. Although machine learning-based surrogate calibration and Bayesian optimization (BO) have been explored to reduce computational burden, SWMM auto calibration that incorporates dimensionality reduction for multi-site, multi-event water-level time series has remained limited. This study proposes a hybrid auto-calibration framework integrating Principal Component Analysis (PCA), Light Gradient Boosting Machine (LightGBM), and Gaussian process-based BO for multi-site, multi-event water-level calibration. Key parameters were identified through Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficient analysis (PRCC), and water-level time series were projected onto a low-dimensional principal-component space. Three strategies were compared: inverse LightGBM mapping (PCs → θ), direct GP-BO (θ → J), and a Hybrid approach combining both. The Hybrid strategy achieved performance comparable to direct BO while reducing SWMM evaluations by approximately 40%, demonstrating improved computational efficiency for identifying influential parameters in urban drainage networks.

Keywords: SWMM, GP-BO, Automatic calibration, LightGBM, PCA, Urban drainage

Acknowledgment

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF). This work is financially supported by Korea Ministry of Land, Infrastructure and Transport (MOLIT) as 「Innovative Talent Education Program for Smart City」. This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Research and Development on the Technology for Securing the Water Resources Stability in Response to Future Change Project, funded by Korea Ministry of Climate, Energy and Environment(MCEE)(RS-2024-00332300). This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Technology development project to optimize planning, operation, and maintenance of urban flood control facilities, funded by Korea Ministry of Climate, Energy and Environment(MCEE)(RS-2024-00398012).