## 4. Discussion
### Machine Learning  
Machine learning methods based on artificial intelligence (AI) offer new approaches for studying compound floods under climate change. These models are built on large amounts of historical data and typically do not require consideration of nonlinear dynamic processes, resulting in better controllability and scalability [107]. Machine learning can predict compound floods without relying on underlying physical properties or extensive computational resources [108], overcoming the limitations of numerical models in simulating large and complex compound floods that require high computational power and time [109]. Currently, the application of machine learning in flood research can be categorized into supervised learning, unsupervised learning, and deep reinforcement learning [110]. Figure 5 provides an overview of machine learning models used in compound flood research within the context of climate change. Common supervised learning algorithms for classification and regression tasks include decision trees (DT), random forests (RF), logistic regression (LR), long short-term memory network (LSTM), convolutional neural networks (CNNs), and support vector machine (SVM). The RF algorithm, which achieves regression and multi classification by constructing many decision trees during the training, has gained attention for its high prediction accuracy and strong non-linear data processing capabilities [111]. For example, [PERSON] and [PERSON] (2022) [112] used the RF algorithm to predict real-time, street-scale, and tidal flood times and locations in Norfolk, VA, USA. Compared to the high-fidelity physical model TUFLOW, the RF model increased computational speed by 3800 times while maintaining an acceptable loss of accuracy. The advantage of the SVM algorithm in flood analysis lies in its robustness to outliers and noise, and the need for fewer computational resources. [PERSON] et al. (2023) [113] applied the SVM to process remote sensing data and evaluate the impact of extreme coastal floods driven by climate change on ecosystems in Canadian coastal areas. CNNs have been applied to extract water bodies and predict storm surges from remote sensing images due to their outstanding ability to detect bright spots in images [114]. For instance, [PERSON] et al. (2021) [115] proposed a CNN and data fusion (DF) framework for mapping compound floods along the southeastern Atlantic coast of the United States. CNNs have also been used in various aspects of compound flood analysis, such as predicting flood depths at different times based on spatial rainfall data [116], extracting 2D wind field information, and predicting storm surge water levels [117]. LSTM can better regulate information flow and preserve remote memory, effectively capturing long-term dependencies in time series data in compound flood scenarios [109]. This algorithm has been applied to predict sea level anomalies during storm surges under different climate change scenarios [118] and to assess flood risks related to changes in river net flow [119]. Unsupervised learning datasets do not have pre-defined labels, and their hidden patterns and structures are discovered by exploring the data itself. In compound flood analysis, typical tasks include clustering, dimensionality reduction, and anomaly detection [110]. Common unsupervised algorithms include K-means clustering (K-means), K-nearest neigh  
Figure 5: Workflow diagram of machine learning models in compound floods under climate change. bors (KNN), hierarchical clustering (HC), and principal component analysis (PCA). For example, [PERSON] et al. (2021) [120] applied PCA and K-means to predict storm surges at numerous geographical locations, while [PERSON] et al. (2023) [121] used K-means to divide the study area into different clusters of flood causes based on disaster environmental factors to achieve rapid prediction of floods in coastal cities. [PERSON] et al.