Improved real-time SWMM flow forecasts using two machine learning approaches

The Storm Water Management Model (SWMM) is a popular and widely used physics-based numerical model for water resource management and flow forecasting. Calibrating SWMM requires a large amount of geospatial and hydro-meteorological data that may be hard to collect, has high uncertainty associated with it, and are often non-stationary. These issues are compounded when modelling large watersheds with several sub-catchments, leading to thousands of parameters that need to be calibrated collectively. The calibration process is time consuming (and often conducted manually), and results in models that are biased, only tuned to specific events, and lead to high uncertainty in the flow forecasts, and thus, limiting their utility.