# Formation Mechanism and Response Strategies for Urban Waterlogging: A Comprehensive Review
## 4 AI Methods for Urban Waterlogging
### Application of AI in Urban Flooding Prediction  
The application of AI technologies in urban flooding prediction primarily involves data integration, risk assessment, model optimization, and intelligent decision-making. Using methods such as deep learning and data mining, AI can efficiently process multi-source data from various sensors, remote sensing images, weather stations, and other sources to identify and predict potential flooding risks in real time.  
Urban flooding prediction relies on various data sources, including accurate precipitation data, land-use information, the status of urban drainage systems, and ground water levels. AI technologies can effectively integrate monitoring data from ground-based meteorological stations and water level sensors. Through model learning, AI can establish spatial and temporal patterns, providing more precise data for urban flooding predictions. [PERSON] et al. [106] proposed the Random Forest algorithm as a predictive tool, which demonstrates significant superiority in classification and regression tasks. The introduction of randomness provides optimized algorithmic support for urban flood prediction. [PERSON] et al. [92] used an automated machine learning (AutoML) approach based on genetic algorithms to develop a rapid early warning model for urban flooding, combining meteorological information for quick flood depth predictions. [PERSON] et al. [93] used the Random Forest model to study the impact of three-dimensional buildings on urban flooding, finding that building density, coverage, and congestion significantly affect the occurrence of flooding events. [PERSON] et al. [94] used Artificial Neural Networks (ANN) to refine precipitation data for the Sydney area, compensating for missing data from unrecorded rain gauge stations. [PERSON] et al. [95] employed machine learning methods to classify urban flooding events in Shenzhen based on rainfall thresholds. [PERSON] et al. [107] proposed using stacked models and hybrid models to assess urban flooding susceptibility in Beijing. Based on data from 266 flooding points and 532 non-flooding points, the study found that stacked models outperformed traditional machine learning models and identified five key indicators highly correlated with flooding, providing important references for urban planning.  
Additionally, AI models can be combined with remote sensing data and GIS technologies for prediction. [PERSON] et al. [16] used a weighted Naive Bayes (WNB) classifier integrated with GIS to assess urban flooding risk in Guangzhou. The results showed that six spatial factors significantly enhanced prediction efficiency, providing valuable insights for urban flood management. [PERSON] et al. [96] used remote sensing and GIS platforms, combined with particle swarm optimization, artificial neural networks, and deep learning neural networks, to develop a flood susceptibility map and predict flood risks in the Kangsabati River Basin in India. The study found that particle swarm optimization had a high AUC value (0.942). [PERSON] et al. [97] employed a machine learning-based REPTree model with Bagging and Random Subspace (RS-REPTree) ensemble framework, using  
Figure 7: Visualization of artificial intelligence methods in waterlogging research.  
GIS for spatial flood susceptibility prediction. The results indicated that the RS-REPTree model had the best predictive ability in flood susceptibility assessment, outperforming the models used individually. [PERSON] et al. [98] used regression models in ArcGIS to predict urban flooding risks in Hanoi, Vietnam, from 2012 to 2018, with an explanatory power of 67.6%, providing significant references for urban planning.