Developing data-driven learning models to predict urban stormwater runoff volume

The Storm Water Management Model (SWMM) is a hydrological model for simulating and predicting runoff. Although powerful, SWMM can be computationally demanding. Therefore, we develop machine learning (ML) models to approximate the behavior of SWMM and expedite the task of predicting runoff. We perform a case study for the First Creek watershed in Knoxville, Tennessee, USA. We train ML models using rainfall data and subcatchment characteristics and apply feature engineering and clustering to objectively compare the outputs from SWMM and ML models. The results show that random forests can predict runoff volume accurately, with a Mean Absolute Error (MAE) of 0.006 (0.001) ${10<^>6}$106 gallons, where predictions are made almost instantaneously. Hence, our proposed ML-based approach can accurately predict runoff while greatly reducing computational requirements, filling a critical need in the field.