# Formation Mechanism and Response Strategies for Urban Waterlogging: A Comprehensive Review
## 4 AI Methods for Urban Waterlogging
### Ongoing Challenges and Future Prospects  
In the study of urban flooding issues, artificial intelligence (AI) has demonstrated exceptional performance in areas such as computational speed, accuracy, and handling complex information. However, AI still faces ongoing challenges when analyzing urban flooding, including issues related to adaptability, data acquisition, model accuracy, and real-time processing capabilities, all of which require continuous improvement. AI models have shown excellent precision and efficiency in predicting flood depth and assessing flood risk in urban areas. For example, a dual-model approach combined with numerical models has significantly outperformed traditional methods in computation speed according to [PERSON] et al. [114], while video IoT-based sensing systems with high real-time capabilities can transmit flood information in a timely manner [115]. AI algorithms excel at processing complex multi-source data from fields such as remote sensing, meteorology, hydrology, and geographic information systems (GIS), and enabling comprehensive flood risk assessments through efficient modeling. For instance, the Random Forest model based on the Boruta algorithm effectively integrates climate patterns and flow levels, improving \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Year and Analysis** & **AI methods** & **Contents** \\ \hline \multirow{4}{*}{[PERSON] et al. [93]} & 2021 Investigating the influence of three-dimensional building configuration on urban pluvial flooding algorithm & \multirow{4}{*}{Random Forest} & \multirow{4}{*}{Discussed the impact of three-dimensional building configuration on urban waterlogging, finding that building density, congestion, and coverage significantly affect waterlogging events.} \\ \cline{2-2}  & 2017 Landslide susceptibility assessment using maximum entropy model with two different data sampling methods & Maximum Entropy Model & \multirow{4}{*}{Used the maximum entropy and two sampling strategies to model landslide susceptibility.} \\ \cline{2-2}  & 2011 Rainfall disaggregation in non-recording gauge stations using space–time information system & Artificial Neural Network & \multirow{4}{*}{Refined precipitation data to compensate for missing data from unrecorded rain gauge stations and evaluated the best data refinement model.} \\ \hline \multirow{4}{*}{[PERSON] et al. [111]} & 2021 Deep learning hybrid model with Boruta-Random forest optimizer algorithm for streamflow forecasting & \multirow{4}{*}{Deep Learning} & Improved flow level prediction accuracy using a deep learning-based feature selection algorithm. \\ \hline \multirow{4}{*}{[PERSON] et al. [112]} & 2020 Evaluation of spatially heterogeneous driving forces of the urban heat environment based on a regression tree model. & Regression Trees & \multirow{4}{*}{Analyzed the spatial heterogeneity impact of urban biophysical components on surface temperature using a regression tree model.} \\ \cline{2-2}  & 2018 A spatial assessment of urban waterlogging risk based on a Weighted Naive Bayes classifier & \multirow{4}{*}{Naïve Bayes Classifier waterlogging risk in Guangzhou.} \\ \cline{2-2}  & 2024 Urban waterlogging susceptibility assessment based on hybrid ensemble machine learning models & \multirow{4}{*}{Stacking Model and Hybrid Model} & \multirow{4}{*}{Evaluated the susceptibility to urban waterlogging in Beijing.} \\ \cline{2-2}  & 2023 Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms & Adaptive Reinforcement Learning & \multirow{4}{*}{Analyzed flood susceptibility in Adana Province, Turkey.} \\ \cline{2-2}  & 2022 Construction of rapid early warning and comprehensive analysis models for urban waterlogging & Automated Machine Learning & \multirow{4}{*}{Constructed an urban waterlogging early warning model.} \\ \hline \hline \end{tabular}
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Table 3: Artificial intelligence methods for waterlogging research. prediction performance [111].