Optimal location of sustainable urban drainage systems to reduce flooding in sewer networks

Urban flooding is a critical issue in rapidly urbanizing areas, primarily driven by the expansion of impervious surfaces. This study presents a methodological framework that integrates optimization algorithms–Simulated Annealing (SA) and Bayesian Optimization (BO)–with the Stormwater Management Model (SWMM) to identify the optimal placement of Sustainable Urban Drainage Systems (SuDS), including permeable pavements, green roofs, and bioretention cells. The optimization process incorporates practical constraints such as budget limits and available implementation area, making the approach broadly applicable to diverse urban contexts. The proposed methodology is demonstrated through a case study in the urban sub-catchment of Chía, Colombia, where flooding volumes were reduced by 12.05% to 18.67% with investments of USD 229,978 and USD 113,328 using SA and BO, respectively. This framework can be readily adapted by urban planners and local authorities to support land-use planning and stormwater management in other municipalities facing similar challenges.