# Formation Mechanism and Response Strategies for Urban Waterlogging: A Comprehensive Review
## 3 Analytical Framework
### Theoretical Research
#### 3.1.2 Waterlogging Prediction Models  
Predicting the occurrence and development of urban waterlogging is an important direction in theoretical research. In recent years, with the acceleration of urbanization and the intensification of climate change, traditional hydrological models and laboratory simulation methods have faced increasing challenges. Researchers have enhanced the accuracy and practicality of waterlogging predictions by analyzing and refining existing models, and by integrating various advanced technologies [48; 49; 50]. Figure 5: Schematic diagram of urban waterlogging due to rain footing [47]. Waterlogging prediction models simulate and analyze urban waterlogging phenomena, trying to find out the impacts of factors such as rainfall, runoff, and model distortion to provide scientific evidence and effective governance plans, thereby strengthening urban resilience against waterlogging. [PERSON] et al. [35] directly applied rainfall intensity to models for experimental validation. [PERSON] et al. [36] reviewed 45 laboratory-based urban flood studies, categorizing and discussing different flow processes and recorded flow variables. By identifying existing datasets and research gaps, they contributed to the application of computational and experimental models in urban flood research. Additionally, [PERSON] et al. [37] assessed the impact of proportional distortion in urban flood experimental models on water depth and flow, indicating that within a reasonable range of distortion, experimental models can still provide effective references for waterlogging predictions when simulating small-scale physical processes of urban flooding. Remote sensing technology and Geographic Information Systems (GIS) are vital tools in modern urban waterlogging prediction. [PERSON] et al. [27] proposed a model combining remote sensing and GIS to analyze the impact of urbanization on runoff in the Pearl River Delta, demonstrating how urbanization exacerbates urban flood risks. High-resolution data enable more precise urban flood modeling. [PERSON] et al. [38] explored how to improve flood modeling using high-resolution data and reviewed methods such as sub-grid parameterization and roughness upsampling to balance efficiency and reliability, thereby enhancing the reliability of flood predictions. [PERSON] et al. [39] reviewed the application of large-scale particle image velocimetry technology in river environments, improving the simulation capacity for complex hydrological processes and providing new insights for urban waterlogging prediction models, which are beneficial for capturing the interactions of different factors. As the impacts of urbanization and climate change on waterlogging become increasingly complex, researchers have adopted ensemble modeling methods to assess waterlogging risks. [PERSON] et al. [42] proposed an ensemble method for urban flood modeling, considering the uncertainty of parameters related to wells, sewage pipes, and surface flow. Their results indicated that ensemble simulations based on overflow and orifice  
Figure 6: Mechanism diagram of urban waterlogging formation. formulas can accurately reproduce experimental data. [PERSON] et al. [43] combined the FEST-WB and WRF models to test two hydrological integrated forecasting systems, finding that optimizing integration strategies helps improve flood warning efficiency. [PERSON] et al. [51] hybrid integrated two novel models (Wavelet-SVR-Bat and Wavelet-SVR-GWO) to assess spatial modeling of urban flood inundation, with these hybrid models significantly outperforming traditional support vector regression in performance. [PERSON] et al. [52] proposed using a multi-model ensemble approach to improve flood loss modeling, evaluating 20 flood loss models and demonstrating that a well-designed multi-model ensemble can provide more accurate loss estimates and reliable probability distributions of uncertainty.