Resumen
Extreme rainfall events cause immense damage in cities where drainage networks are nonexistent or deficient and thus unable to transport rainwater. Infrastructure adaptations can reduce flooding and help the population avoid the associated negative consequences. Consequently, it is imperative to develop suitable mathematical models rooted in a thorough understanding of the system. Additionally, the utilization of efficient computational search techniques is crucial when applying these methods to real-world problems. In this study, we propose a novel iterative search space reduction methodology coupled with a multiobjective algorithm (NSGA-II) for urban drainage network rehabilitation and flood mitigation. This approach considers the replacement of pipes and the installation of storm tanks (STs) in drainage networks. Additionally, NSGA-II is integrated with the Storm Water Management Model (SWMM) to achieve multiobjective optimization. To demonstrate the advantages of using this technique, two case study networks are presented. After three iterations, 90% of the decision variables are eliminated from the process in the E-Chicó case, and 76% are eliminated in the Ayurá case. The primary outcome of this study is that the proposed methodology yields reductions in rehabilitation costs and flood levels. Additionally, the application of NSGA-II to the reduced-dimension model of the network yields a superior Pareto front compared to that of the original network.