Incorporating spatial heterogeneity information into multi-objective optimization methodology of green infrastructure

Green infrastructures (GIs), serving as a complement to grey infrastructures in urban stormwater management, have been widely adopted due to their sustainability, resilience, and adaptability. Given the diverse types, parameters, and combinations of GIs, it is essential to use multi-objective optimization to balance conflicting environmental and economic goals. However, few optimization methodologies incorporate spatial heterogeneity information. The novelty of our research is (1) enhancing the "Model + optimization + decision-making" optimization framework of GIs and (2) incorporating spatial heterogeneity into GIs multi-objective spatial optimization. In this study, a novel multi-factor spatial heterogeneity adaptation optimization framework (MFSHAOF) was proposed to refine regional adaptability of existing GIs multi-objective optimization methods by parameterizing objective weights using a factor-based strategy. Multi-factor was quantified in terms of urban floods, Non-point Source (NPS) pollution, and economic constraints at a subdistrict level using hydrological and water quality model simulation, and socio-economic data mining. Then, a multi-factor adaptation GIs optimal scheme was determined using a multi-objective optimization model and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Suzhou urban district (a provincial pilot "Sponge City" in Jiangsu, China) was studied. Study area was divided into three clusters: Cluster I (flood-dominated), Cluster II (NPS pollution-dominated), and Cluster III (economy-dominated). Subsequently, Pingjiang New City in Cluster III, was selected to demonstrate the determination of the GIs optimal scheme for multi-factor adaptation. The results showed that incorporating spatial heterogeneity into GIs multi-objective optimization process enhanced runoff control by 5.68% and pollutant reduction by 13.88%, therefore meeting both the local runoff control and pollutant reduction goals.