Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model

Urban pluvial floods induced by rainstorms can cause severe losses to human lives and property. Fast and accurate simulation and prediction of urban pluvial flood are of significance for disaster prevention and mitigation. However, physics-based models still experience excessive computational time when used for flood simulation and prediction. In this study, we explore a deep learning (DL) approach employing convolutional neural networks (CNN) as an alternative model to achieve fast prediction of urban floods. We first cluster a rainstorm-inundation database generated using a physics-based coupled model, then we develop a CNN model for predicting the spatial and temporal evolution of rainstorm inundation, and finally we compare the effectiveness of the CNN model with that of the coupled model and three other classical machine learning (ML) models. The results show that: 1) The inundation water depths predicted using the CNN model are close to those predicted using the coupled model, and the average PCC, MAE and RMSE metrics under a test rainstorm reach 0.983, 0.020 m and 0.086 m, respectively. 2) The CNN model reproduces well the trend of water depth in each model grid cell over time, especially for a heavily inundated grid. 3) The predicted effectiveness of the CNN model outperforms an extreme gradient boosting (XGBoost) model, followed by a multi-objective random forest (MORF) model and K-nearest neighbor (KNN) model. 4) The computational speed of the CNN model is extremely fast. The model can simulate inundation water depth with a spatial resolution of 8 m by 8 m (about 74 km2) and a temporal resolution of 30 min for a 6-hour lead time within 12 s, which is 600 times faster than that of a coupled model. We confirm that the CNN model with clustering method is a powerful surrogate model for fast simulation of urban pluvial flood, providing an important reference for the use of DL in early warning and mitigation in relation to urban flood disasters.