# Formation Mechanism and Response Strategies for Urban Waterlogging: A Comprehensive Review
## 4 AI Methods for Urban Waterlogging
### Application of AI in Flood Risk Assessment  
Another significant application of AI technology in the context of flooding is risk assessment and emergency response. AI can conduct quantitative assessments of flood disaster risks based on real-time data and historical information, spatially assess flood susceptibility, evaluate the severity of flooding in different urban areas, and propose emergency measures such as traffic diversion, drainage strategies adjustments, and the mobilization of rescue resources [83, 108, 109].  
By analyzing meteorological, geological, and hydrological data with artificial intelligence techniques, it is possible to predict the likelihood and impact of flooding, providing a scientific basis for urban planning and risk assessment, thereby enhancing urban resilience and response efficiency. [PERSON] et al. [99] used Shanghai's subway system as an example, employing a combination of Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Clustering Analysis (FCA) to assess flood risks in a subsiding environment. The results indicated that over 30% of subway lines face high flood risk, suggesting the need for more detailed consideration of subsidence factors to mitigate risks. [PERSON] et al. [100] analyzed the risk assessment of urban stormwater systems using neural networks and dynamic hydraulic models, introducing a fuzzy comprehensive evaluation method to establish a flood risk assessment framework, supporting drainage planning and emergency management in sponge cities and mapping urban stormwater and flood risks. [PERSON] et al. [101] predicted urban flood risks in Shenzhen by integrating hydrometeorological data, urban surface characteristics, and building configuration factors, with results showing that LightGBM performed best, with the main influencing factors being rainfall and building density. [PERSON] et al. [102] constructed a flood risk assessment framework using the MaxEnt model to analyze flood risks in Tianjin, revealing that population density and impervious surfaces are the primary influencing factors, with high-risk areas concentrated in the city center, providing theoretical support for urban planning and disaster warning.  
In addition to short-term risk assessments, AI is also utilized in the long-term planning of urban drainage systems, identifying bottlenecks and potential risk points within the system and proposing corresponding optimization solutions. For example, AI-based simulation and optimization models can assist urban planners in designing new drainage networks, allowing for the rational layout of drainage facilities and enhancement of drainage capacity to avoid future flooding issues. [PERSON] et al. [103] proposed a new artificial intelligence model, Bagging-LMT, for flood susceptibility mapping. By generating a spatial database that includes flood inventories and flood condition factors, the model's assessment results showed it outperformed four other benchmark models, making it suitable for sustainable management in flood-prone areas. [PERSON] et al. [104] used machine learning algorithms to study the dominant factors of flooding in different regions, integrating spatial heterogeneity theory to propose regionally adaptive disaster reduction strategies. [PERSON] et al. [105] systematically explored the dynamic characteristics of the urban integration process through spatial clustering techniques, landscape metrics, and circular buffer analysis, with the successful experience of the Guangzhou-Foshan integration providing references for similar cases.