Multi-objective Optimization of Nature-based Solutions

Urbanization and climate change have exacerbated many urban challenges such as flooding, water pollution and urban heat islands. Nature-based solutions (NBS) have been proposed as nature-inspired and cost-effective solutions that provide environmental, social, and economic benefits while tackling urban challenges. This research aims to identify multiple benefits of NBS and optimize NBS design under the local conditions. The hydrology-hydraulic model was built in Storm Water Management Model (SWMM) to simulate performance before and after NBS scenarios. Adopting a multi-objective optimization algorithm and deriving the Pareto front solution set of NBS scenarios are core methods in this multi-objective optimization procedure, and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) will be used. Thus, this research will further discuss the method to reduce dimensionality in building functions for multiple objectives optimization. The methodology was applied to a case study in Tivoli Park, Ljubljana, Slovenia, where various land-use types have been involved and frequent flooding problems have occurred. The optimized results demonstrate that NBS scenarios are effective for flood reduction, peak flow control, pollutant reduction, water reuse, infiltration increase, evaporation increase, and green space increase. This research provides an interpretation and explanation of the relationship between trade-offs and NBS scenarios.