Mitigating the frequency and severity of Combined Sewer Overflows (CSOs) represents a significant engineering and economic challenge in urban stormwater management (SWM). Low-Impact Development (LID) methods are a decentralized approach for dealing with this challenge. Current methods for estimating CSO mitigation efficacy and informing choices about infrastructure solutions are typically based on simulation of the storm sewer network for municipalities. The recent public availability of rainfall and CSO data represents a potential opportunity to improve the quality of these estimates, as well as reducing the time it takes to generate them.

A novel decision support model is presented which solves a Mixed Integer Program (MIP) formulation of the Low-Impact Development Rapid Assessment (LIDRA) method algorithmically to identify priority catchment areas for intervention with LID infrastructure, as well as the optimal extent of investment, subject to different budgetary constraints. The reliability of the model is improved by means of a Monte Carlo simulation.

This method is demonstrated with an open dataset from the city of Spokane, Washington, but it is generalizable to other municipalities where storm and CSO data is available.