## 1 Introduction
### Current Research Status
#### 1.2.2 Research Status of SWMM  
As urban flooding becomes more and more serious, accurate simulation of urban flooding has become indispensable for reducing it. After 30 years of development, the SWMM has been widely used by relevant departments around the world for urban stormwater pollution and urban drainage management [20]. The SWMM (Storm Water Management Model) was developed by the US Environmental Protection Agency (EPA) in the 1970s. It stimulates the flow of water in pipes by solving the Saint-Venant equation system. It can analyze the current state of drainage systems, plan the underlying surface, simulate water quality, design drainage networks, and have additional LID functions. It has been widely used in fields such as municipal planning and scientific research [7; 8; 9; 10]. [PERSON] et al. 's study applies a high-resolution SWMM with genetic parameter optimization, enhancing stormwater management accuracy in complex urban environments [21]. [PERSON] and [PERSON] optimize SWMM parameters for improved runoff calibration, offering en -hanced precision in environmental impact assessments of urban water systems [22]. [PERSON] et al. integrate Morris and GLUE methods for SWMM parameter optimization, which improves model robustness in urban runoff and water quality prediction [23]. [PERSON] et al. proved that new optimization strategies for the SWMM are introduced for urban stormwater quality applications, contributing to more effective urban water quality management [24]. [PERSON] et al. 's study uses an optimization algorithm to calibrate SWMM parameters, demonstrating improved accuracy in urban stormwater simulation [25]. [PERSON] et al. 's paper proposes an automatic calibration method for SWMM using differential evolution and Bayesian optimization, which enhances parameter calibration efficiency [26]. [PERSON] et al. present a tool for automatic SWMM calibration, which simplifies and improves the model calibration process in stormwater applications [27]. [PERSON] et al. focus on automated calibration techniques applied to SWMM RUNOFF, facilitating more efficient urban runoff modelling [28]. [PERSON] et al. 's research demonstrates the automatic calibration of the SWMM for a large urban catchment, supporting improved stormwater management in complex urban areas [29]. [PERSON] et al. 's study applies automated SWMM calibration to PESTtor, a small suburban catchment, enhancing localized stormwater modelling accuracy [30]. [PERSON] and [PERSON] study employs genetic algorithms for SWMM calibration, introducing a robust method for optimizing model performance in urban drainage applications [31]. [PERSON] et al. focus on sensitivity analysis and calibration of SWMM for urban green land types, offering insights into runoff simulation for different urban greenspaces [32]. [PERSON] and [PERSON]'s study mainly focuses on genetic algorithms applied to optimize SWMM for runoff quantity and quality in a eutrophic lake watershed, highlighting improved modelling for water quality management [33]. [PERSON] et al. prove that sensitivity analysis of SWMM parameters enhances understanding of urban runoff in semi-arid areas, adapting the model for diverse climatic conditions [34]. [PERSON] et al. prove that SWMM calibration and validation in two urban catchments in Athens provide practical insights into model application in Mediterranean climates [35]. [PERSON] et al. focus on spatial layout optimization for green infrastructure using SWMM and life-cycle multi-objective algorithms to enhance sustainable urban water management [36]. [PERSON] et al. present a simultaneous optimization approach for SWMM parameters, supporting enhanced stormwater management through multi-objective functions [37]. [PERSON] et al. introduce Bayes_Opt-SWMM, a Gaussian process-based Bayesian optimization tool developed for real-time flood modelling, which contributes to dynamic flood response strategies [38]. [PERSON] et al.