A dynamic model integrating ensemble particle filtering and gradient boosting decision tree algorithms for urban flood forecasting.

Accurate and timely flood forecasting is essential for issuing effective early warnings and reducing casualties as well as economic losses. However, urban flood forecasting models often struggle to balance computational efficiency with nonlinear representation capability. Forecast errors are rapidly amplified during highly dynamic flooding processes. Assimilating observational data into the model can correct forecast trajectories and reduce overall uncertainties. Existing mainstream data assimilation techniques, such as the ensemble Kalman filter and particle filter, can hardly balance the requirements of nonlinearity and timeliness in urban flood forecasting. Furthermore, effectively utilizing limited observed data to balance the assimilation update frequency and forecast accuracy is critical. To address these challenges, a dynamic urban flood forecasting model coupling the ensemble particle filter with the gradient boosting decision tree was proposed in this study. The performance of state-only and joint state-hyperparameter assimilation for flood prediction at typical ponding points in the central urban area of Zhengzhou was compared. The results indicate that data assimilation improves flood forecast accuracy. State-only assimilation reduces the root mean square error from 0.051-0.084 m to 0.009-0.031 m, while increasing the mean Kling-Gupta efficiency from 0.794 to 0.961. The underlying mechanism whereby incorporating hyperparameters into state variables fails to achieve further performance improvement is analyzed. When the assimilation update frequency is 50 min, an optimal balance between forecast accuracy and observational cost is achieved. Using the sliding window mechanism to identify effective forecast windows for different ponding points provides important support for urban flood emergency decision-making.