As climate change increases the occurrences of extreme weather events, like flooding threaten humans more often. Hydrodynamic models provide spatially distributed water depths as inundation maps, which are essential for flood protection. Such models are not computationally efficient enough to deliver results before or during an event. To ensure real-time prediction, we developed a feature-informed data-driven forecast system (FFS), which interpreted the forecasting process as an image-to-image translation, to predict the maximum water depth for a fluvial flood event. The FFS combines a convolutional neural network (CNN) and feature-informed dense layers to allow the integration of the distance to the river of each cell to be predicted into the FFS. The aim is to ensure training for the whole study area on a standard computer. A hybrid database with pre-simulated scenarios is used to train, validate, and test the FFS. The FFS delivers predictions within seconds making a real-time application possible. The quality of prediction compared with the results of the pre-simulated physically-based model shows an average root mean square error (RMSE) of 0.052 for thirty-five test events, and of 0.074 and 0.141 for two observed events. Thus, the FFS provides an efficient alternative to hydrodynamic models for flood forecasting.
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