Rapid SWMM Catchment Prototyping Using Fuzzy Logic: Analyzing Catchment Features for Enhanced Efficiency

Parameterization of SWMM subcatchments is labor-intensive and a major source of model uncertainty. This study presents the Rapid Catchment Generator (RCG), a fuzzy logic framework that derives hydraulic width, average slope, and impervious fraction from three easily accessible descriptors—area, landform type, and land cover type—and inserts them directly into SWMM input files. A sensitivity analysis of 116,640 synthetic simulations confirmed that width, slope, and imperviousness are the dominant controls on runoff and infiltration. Their relationships are encoded in triangular membership functions covering nine geomorphic classes and twelve imperviousness classes, linked through expert-calibrated Mamdani rules. Validation on a calibrated 37-subcatchment, 10-hectare urban basin in Wrocław, Poland, showed Mean Absolute Percentage Errors of 15.9–16.0% for total runoff, 19% for infiltration, and 29–37% for peak flow, while preserving hydrograph shape. RCG thus reduces model setup time and provides a transparent, reproducible starting point for rapid scenario screening and subsequent fine-scale calibration.