U-FLOOD - Topographic deep learning for predicting urban pluvial flood water depth

This study investigates how deep-learning can be configured to optimise the prediction of 2D maximum water depth maps in urban pluvial flood events. A neural network model is trained to exploit patterns in hyetographs as well as in topographical data, with the specific aim of enabling fast predictions of flood depths for observed rain events and spatial locations that have not been included in the training dataset. A neural network architecture that is widely used for image segmentation (U-NET) is adapted for this purpose. Key novelties are a systematic investigation of which spatial inputs should be provided to the deep learning model, which hyper-parametrization optimizes predictive performance, and a systematic evaluation of prediction performance for locations and rain events that were not considered in training. We find that a spatial input dataset of only 5 variables that describe local terrain shape and imperviousness is optimal to generate predictions of water depth. Neural network architectures with between 97,000 and 260,000,000 parameters are tested, and a model with 28,000,000 parameters is found optimal. U-FLOOD is demonstrated to yield similar predictive performance as existing screening approaches, even though the assessment is performed for natural rain events and in locations unknown to the network, and flood predictions are generated within seconds. Improvements can likely be obtained by ensuring a balanced representation of temporal and spatial rainfall patterns in the training dataset, further improved spatial input datasets, and by linking U-FLOOD to dynamic sewer system models.