# Formation Mechanism and Response Strategies for Urban Waterlogging: A Comprehensive Review
## 4 AI Methods for Urban Waterlogging
### Ongoing Challenges and Future Prospects
AI models also quantify the main driving factors of urban flooding, providing a scientific basis for optimizing urban building configurations and formulating disaster reduction strategies. Key factors such as building density, vegetation coverage, and rainfall are critical to flooding. By adjusting these variables, flood risk can be effectively reduced [93]. Additionally, AI supports the development of rapid early warning systems, enhancing both the coverage and response speed of flood risk predictions. Early warning models based on AutoML can adapt to flood prediction needs in areas lacking monitoring data, such as water level stations [92]. Based on the Sections discussed earlier in the article, the framework for the warning mechanism of urban waterlogging is illustrated in Figure 8. The application of artificial intelligence in flood forecasting can accelerate data analysis of predictive models, thus speeding up the overall iterative process and enhancing the rate of generation for this emergency response increment. The application areas of artificial intelligence in current research on urban waterlogging, as explored in our review article, are presented in Figure 9.  
Despite the success of AI models in specific regions, their adaptability in different areas, scenarios, and climate conditions still needs improvement. For example, flood-driving factors may vary significantly across different geographical conditions, requiring regional optimization of models [107]. Furthermore, AI models, particularly deep learning models, are often criticized for their \"black-box\" nature, which increases the difficulty of interpreting results. This becomes a significant barrier when it comes to policy decisions and public trust. While interpretability tools like SHAP analysis can partially mitigate this issue, their scope and applicability are limited [113]. High-precision AI models typically require complex computational resources, which may exceed the infrastructure capacity of some cities. In real-time applications, the cost of large-scale data processing can be high, requiring further optimization of model efficiency [115]. Figure 8: Framework diagram of the emergency response process. Looking to the future, the application of AI in urban flooding can focus on the following directions: developing low-cost, high-performance models by improving algorithms and computational architectures to reduce the resource demands of models, enhancing model adaptability across various scenarios; promoting interdisciplinary research by integrating AI with knowledge from meteorology, hydrology, ecology, and other fields to explore more comprehensive solutions; developing AI tools with higher transparency and interpretability to make them more acceptable to policymakers and the public; and enhancing regional adaptability by optimizing AI models for the specific needs of different regions, improving their generalization capabilities. The research methods under different urban conditions could be analyzed in greater depth. For instance, integrating GIS with deep learning models can lead to better predictions of the effectiveness of stormwater management measures in areas with abundant green spaces. In transportation hubs, combining real-time data and machine learning algorithms to optimize traffic diversion strategies can mitigate the impact of flooding on transport. Finding suitable models for various scenarios helps urban flooding management personnel in making informed decisions.