## 1 Introduction
### Current Research Status
#### 1.2.2 Research Status of SWMM
's research integrates the SWMM with particle swarm optimization for urban runoff water quality control, emphasizing the role of green infrastructures [39]. [PERSON] et al. 's study combines the SWMM and field data for efficient urban runoff and water quality modelling in Tehran, highlighting adaptability in large urban watersheds [40]. [PERSON] et al. focus on OSTRICH-SWMM, a multi-objective optimization tool introduced for planning green infrastructure, advancing SWMM's application in sustainable urban design [41]. [PERSON] et al. 's paper examines the impact of SWMM catchment discretization and informs best practices for stormwater modelling in urban areas [42]. [PERSON] et al. apply geo-informatics to estimate SWMM sub-catchment parameters, improving parameterization accuracy for stormwater management [43]. [PERSON] et al. 's study concerns an auto-calibrated SWMM, which explores low-impact development strategies and promotes sustainable stormwater solutions in highly urbanized areas [44]. [PERSON] et al. prove that the spatial optimization approach for LID practices based on the SWMM-FTC improves cost-effectiveness in urban stormwater infrastructure [45]. [PERSON] et al. 's study employs a multi-objective optimization model for automatic SWMM calibration, advancing model efficiency in complex urban settings [46]. [PERSON] and [PERSON] combine the SWMM with genetic algorithms, enhancing rainfall-runoff model calibration and sensitivity analysis [47]. [PERSON] et al. demonstrate that real-time correction and dynamic urban flood simulation can be performed in the SWMM, which supports responsive flood management strategies [48]. [PERSON] and [PERSON] demonstrate that neural networks can be applied to the SWMM for sensitivity-calibration analysis, innovating parameter optimization in stormwater modelling [49]. While SWMM-based research predominantly addresses technical challenges in urban flood management, studies in Jordan, one of the most water-scarce countries globally, highlight the role of political patronage and shadow actors in shaping water policy, emphasizing governance-related inefficiencies rather than purely technical solutions. In contrast, the present study focuses on the optimization and application of the SWMM to address urban flooding issues in China, where the challenges are primarily technical and hydrological in nature [50]. Furthermore, efficient algorithmic approaches for parameter optimization have the potential to provide more accurate simulations and improve decision-making in urban stormwater management. This study focuses on optimizing the drainage network parameters with the aim of improving the flood resilience of the local drainage network [4]. Additionally, it compares various optimization algorithms to determine the most effective approach for enhancing network performance. This is particularly important given the increasing frequency of extreme rainfall events and the need for resilient urban infrastructure [51]. By addressing deficiencies in the existing drainage network, this study seeks to contribute to reducing the flood vulnerability of urban areas in China and beyond.