## 4. Discussion
### Coupling Models  
The three types of models have shown distinct advantages and disadvantages in studying compound floods, and researchers are increasingly attempting to conduct model coupling to better assess compound flood risks. [PERSON] et al. (2019) [133] proposed a combined approach that integrates statistical analysis with hydrodynamic modeling, using bivariate statistical analysis to create river flow and sea level combinations for hydrodynamic modeling. The extreme water levels simulated by the hydrodynamic model were integrated to systematically evaluate the risk of coastal compound floods. [PERSON] and [PERSON] (2022) [134] used thousands of synthetic tropical cyclones to characterize the joint probability of rainfall and storm surges, and simulated it in a hydrodynamic model, incorporating sea level predictions into the probability. Their research showed that, due to the effects of climate change, the 100-year flood extent and inundation volume in the Cape Fear Estuary, United States, increased by 27% and 62%, respectively. While high-resolution numerical models can accurately simulate compound floods caused by rainfall, storm surges, and sea level rise, they incur high computational costs. Combining statistical and numerical models provides an efficient approach to describing compound flood disasters. [PERSON] et al. (2022) [135] used the output of compound flood scenarios simulated by hydrodynamic models to train three ML models: RF, multiple linear regression (MLR), and SVM, ultimately achieving prediction of future floods in the Puas River Delta. RF proved to be the most accurate algorithm for simulating regional water level dynamics due to its successful prediction of 11 out of 17 compound flood events. The combination of numerical models and ML models offers a promising solution for addressing compound flood disasters in data-scar areas.  
Traditional numerical simulation models for predicting compound floods require substantial computational resources and large volumes of observational data. Although these models achieve high accuracy, they are limited by high computational costs and the sensitivity of parameter settings. ML addresses some of these limitations by not relying on physical models, offering faster computation speeds, and being well-suited for handling complex nonlinear relationships. Integrating numerical simulations with ML leverages the strengths of both approaches, enhancing the accuracy and speed of compound flood predictions, especially in data-scar regions. This integrated framework includes four key components: (1) numerical simulation-driven ML training, in which numerical models simulate flood scenarios, and the outputs are used to train ML models, enabling them to predict future compound flood events; (2) parallel operation, in which ML models and numerical simulations run concurrently, generating separate predictions that are combined, through weighted averaging or other methods, to improve overall prediction accuracy; (3) risk identification and targeted simulation, in which ML models rapidly identify potential high-risk flood areas, after which numerical simulations perform detailed, fine-grained modeling in these regions, reducing the computational burden of large-scale simulations; and (4) parameter optimization, in which ML models predict key parameters for numerical simulations, which are then fed back into the numerical models to rerun simulations, further enhancing prediction accuracy.