Conference Agenda

Team name: Sewer or Later Prabhat Joshi, Matthew McGauley, Ricardo Reyes Sotomayor, Fabrizia Fappiano, Giovan Battista Cavadini The proposed approach for retrofitting urban drainage networks with nature-based solutions (NBS) consists of two key steps. In the first step, potential NBS implementation solutions are filtered and constrained using the objective functions for cost and biodiversity, as these objectives are independent of SWMM simulation outputs. This pre-selection ensures that only feasible combinations, which adhere to cost constraints and incorporate diverse NBS elements that enhance biodiversity, are considered. In the second step, optimization is performed by integrating the Python packages PySWMM (McDonnell et al., 2020) and Pymoo (Blank and Deb, 2020). This integration allows for optimizing the placement and extent of NBS elements based on SWMM simulation outputs and the defined objective functions. PySWMM enables dynamic simulation of SWMM input files within Python, while Pymoo supports the use of multi-objective evolutionary algorithms, for instance the NSGA-II algorithm (Deb et al., 2002). This method has been effectively utilized for SWMM model calibration (Rodriguez et al., 2024) and for cost optimization of NbS in flood prevention (Ur Rehman et al., 2024); however, it has yet to be applied for optimizing multiple objectives of NbS simultaneously. This method would offer a framework for balancing stormwater management goals with cost efficiency and biodiversity enhancement in urban environments, by selecting the solution that maximizes the objective functions.