floodGAN – A deep learning-based model for rapid urban flood forecasting

Recent urban flood events revealed how severe and fast the impacts of heavy rainfall can be. Pluvial floods pose an increasing risk to communities worldwide due to ongoing urbanization and changes in climate patterns. Still, pluvial flood warnings are limited to meteorological forecasts or water level monitoring which are insufficient to warn people against the local and terrain-specific flood risks. Therefore, rapid flood models are essential to implement effective and robust early warning systems to mitigate the risk of pluvial flooding. Although hydrodynamic (HD) models are state-of-the-art for simulation pluvial flood hazards, the required computation times are too long for real-time applications.

In order to overcome the computation time bottleneck of HD models, the deep learning model floodGAN has been developed. FloodGAN combines two adversarial Convolutional Neural Networks (CNN) that are trained on high-resolution rainfall-flood data generated from rainfall generators and HD models. FloodGAN translates the flood forecasting problem into an image-to-image translation task whereby the model learns the non-linear spatial relationships of rainfall and hydraulic data. Thus, it directly translates spatially distributed rainfall forecasts into detailed hazard maps within seconds. Next to the inundation depth, the model can predict the velocities and time periods of hydraulic peaks of an upcoming rainfall event. Due to its image-translation approach, the floodGAN model can be applied for large areas and can be run on standard computer systems, fulfilling the tasks of fast and practical flood warning systems.

To evaluate the accuracy and generalization capabilities of the floodGAN model, numerous performance tests were performed using synthetic rainfall events as well as a past heavy rainfall event of 2018. Therefore, the city of Aachen was used as a case study. Performance tests demonstrated a speedup factor of 10⁶ compared to HD models while maintaining high model quality and accuracy and good generalization capabilities for highly variable rainfall events. Improvements can be obtained by integrating recurrent neural network architectures and training with temporal rainfall series to forecast the dynamics of the flooding processes.