Integrated SUSTAIN-SWMM-MCDM Approach for Optimal Selection of LID Practices in Urban Stormwater Systems

Abstract Rapid urbanization has increased impervious areas, leading to a higher flood hazard across cities worldwide. Low Impact Development (LID) practices have shown efficacy in reducing urban runoff; nevertheless, choosing the best combinations in terms of implementation cost and performance is of great importance. The present study introduces a framework based on green infrastructure, multi-objective optimization, and decision support tools to determine the most cost-effective LID solutions. The Storm Water Management Model (SWMM) was employed for rainfall-runoff and hydraulic modeling in Region 1, District 11 of Tehran, Iran. Six scenarios of different combinations of LID practices were developed. The system for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) was used to optimize and evaluate each scenario. The selected solutions were imported to the SWMM to evaluate the stormwater system performance. Then, two multi criteria decision making (MCDM) models, including TOPSIS and COPRAS, were employed to rank the scenarios based on four technical and economic criteria. Results showed that scenario 4, consisting of rain barrels, porous pavements, and vegetated swales, had the best performance under TOPSIS with a 7.68 million USD and reduced the runoff volume and peak flow by 20.77% and 19.2%, respectively. However, Under the COPRAS method, Scenario 2 with a combination of rain barrels, bio-retention cells, and vegetated swales showed higher performance than the other scenarios with 3.25 million USD and led to a 15% reduction in the runoff volume and 4.30% in the peak flow. The COPRAS method was more sensitive to cost weights and chose the most economical scenario as the ideal. However, Scenario 4 concluded to be more feasible due to spatial limitations in the study area. The proposed SWMM—SUSTAIN—MCDM framework could be helpful to decision-makers in the design, performance evaluation, cost estimation, and selection of optimal scenarios.