Risk-Driven Multi-Objective Synergistic Optimization of Grey-Green Infrastructure in High-Density Urban Areas

High-density urban areas face a critical trade-off between limited land resources and intensifying flood risks. This study develops a grey-green infrastructure (GGI) optimization framework that integrates hazard–exposure–vulnerability (H-E-V) risk assessment, surrogate modelling, and NSGA-III to simultaneously minimize cost, maximize flood control, and enhance water environmental benefits. The Suqian City case study reveals: (1) Grey-green coupling significantly outperforms single green infrastructure (GI), providing an additional 7.07–23.34 percentage points in flood risk control rate (FRCR). While GI reaches a performance bottleneck at 78.59% FRCR under extreme events, the GGI configuration maintains a high efficiency of >92.74%. (2) Risk-informed spatial targeting effectively reclassifies urban vulnerability. Under a 20-year return period, high-risk and medium-high risk areas are reduced by 80.99% and 52.15%, respectively. The validated surrogate models ensure high optimization efficiency with R2 values exceeding 0.85. This framework provides a methodologically transferable decision-support tool for sponge city construction, demonstrating that strategic spatial allocation is as vital as infrastructure capacity for urban flood risk management.