A Simple and Interpretable Random Forest Framework for Transferable Rapid Urban Flood Simulation

Urban flood modelling faces significant challenges when applied to rainfall events for which observational data are scarce, thereby limiting the reliability of flood forecasts under unseen conditions. Enhancing model transferability is therefore essential for effective flood hazard assessment and emergency response, yet this issue remains insufficiently addressed in current urban flood research. Recent advances in machine learning offer promising opportunities to improve flood model transferability while preserving computational efficiency and interpretability. In particular, ensemble-based methods such as Random Forest (RF) models demonstrate robust performance with limited training data and provide valuable insights into model behaviour.

This study presents a simple and interpretable RF-based framework for transferable urban flood simulation, developed for the city of Antwerp. The model is trained using spatial inundation depth data generated by a detailed hydrodynamic model, relying on a limited set of input variables, including digital elevation, land cover, and radar rainfall information. Training is performed on one historical rainfall event and evaluated on an independent event to assess transferability. To improve adaptation to unseen rainfall conditions, spatial fine-tuning is applied using only 10% of the flood impact data from the target event.

The proposed framework achieves strong predictive skill, with Nash–Sutcliffe efficiency values exceeding 0.77 and Kling–Gupta efficiency above 0.87, while enabling rapid predictions over large urban domains. Comparative analyses further show that the RF-based approach consistently outperforms alternative machine learning models under both transfer and uncertainty scenarios.

Overall, this study demonstrates that a classic RF model can deliver an efficient, transferable, and interpretable solution for rapid urban flood simulation, supporting improved flood risk management and emergency decision-making.