Multi-objective optimization of nature-based solutions in urban stormwater management: A scoping review

Multi-objective optimization (MOO) methods are increasingly used to optimize the multifunctional benefits and costs of Nature-based solutions (NbS) in urban water management. However, comprehensive reviews of current research in this area remain limited. This scoping review analyzed 110 optimization studies focused on NbS in urban water management. It examined key components of the simulation-optimization (S/O) framework, including simulation models, optimization algorithms, objectives, spatial and temporal configurations. Twenty-three algorithms were assessed for their applications, strengths and limitations. NSGA-II was the most widely used, while advanced algorithms were applied in a few studies for complex, high-dimensional problems. Water quantity (87%) and costs (93%) were the most studied objectives, whereas only 11% of studies addressed other socio-environmental objectives. Various challenges in the S/O framework, such as integrating socio-environmental benefits, model complexity, uncertainties and optimal solution selection, are discussed. Future research should prioritize proper objectives selection, socio-environmental objectives integration and advanced dynamic NbS planning. • Analyzes the impact of components of simulation-optimization framework on model performance and computational complexity. • Reviews the strengths and weaknesses of optimization algorithms. • Identifies challenges encountered in the simulation-optimization framework and provides insights to resolve them. • Recommends future research on finding the appropriate objectives and on methods to integrate them into the optimization algorithm.