  Most of the two-dimensional (2D) hydraulic/hydrodynamic models are still
computationally too demanding for real-time applications. In this paper, an
innovative modelling approach based on a deep convolutional neural network
(CNN) method is presented for rapid prediction of fluvial flood inundation. The
CNN model is trained using outputs from a 2D hydraulic model (i.e. LISFLOOD-FP)
to predict water depths. The pre-trained model is then applied to simulate the
January 2005 and December 2015 floods in Carlisle, UK. The CNN predictions are
compared favourably with the outputs produced by LISFLOOD-FP. The performance
of the CNN model is further confirmed by benchmarking against a support vector
regression (SVR) method. The results show that the CNN model outperforms SVR by
a large margin. The CNN model is highly accurate in capturing flooded cells as
indicated by several quantitative assessment matrices. The estimated error for
reproducing maximum flood depth is 0 ~ 0.2 meters for the 2005 event and 0 ~
0.5 meters for the 2015 event at over 99% of the cells covering the
computational domain. The proposed CNN method offers great potential for
real-time flood modelling/forecasting considering its simplicity, superior
performance and computational efficiency.
