A Rapid Prediction Method for Key Information of the Urban Flood Control Engineering System Based on Machine Learning: An Empirical Study of the Wusha River Basin

With the intensification of global climate change, the frequency and intensity of urban flood disasters have been increasing significantly, highlighting the necessity for a scientific assessment of urban flood risks. However, most existing studies focus primarily on the spatial distribution of urban flood data and their socio-economic impacts, with limited attention on the urban flood control engineering system (UFCES) itself and the analysis of urban flood risks from the perspective of the degree of system failure. To address this gap, we proposed a rapid prediction method for key information of the UFCES based on a machine learning model. With the aim of improving the accuracy and timeliness of information prediction, we employed a coupled modeling approach that integrates physical mechanisms with data-driven methods to simulate and predict the information. Taking the Wusha River Basin in Nanchang City as a case study, we generated the training, validation, and testing datasets for machine learning using the urban flood mechanism model. Subsequently, we compared the prediction performance of four machine learning models: random forest (RF), XGBoost (XGB), support vector regression (SVR), and the backpropagation neural network (BP). The results indicate that the XGB model provides more stable and accurate simulation outcomes for key information, with Nash coefficient (R2) values above 0.87 and relative error (RE) values below 0.06. Additionally, the XGB model exhibited significant advantages in terms of simulation speed and model generalization performance. Furthermore, we explored methods for selecting key information indicators and generating samples required for the coupled model. These findings are crucial for the rapid prediction of key information in the UFCES. These achievements improve the technical level of urban flood simulation and provide richer information for urban flood risk management.