A deep convolutional neural network for rapid fluvial flood inundation modelling

06/20/2020 ∙ by Syed Kabir, et al. ∙ 0

The two-dimensional (2D) hydrodynamic models are often infeasible for real-time operations. In this paper, a deep convolutional neural network (CNN)-based method is presented for rapid fluvial flood modelling. The CNN model is trained using outputs from a two-dimensional hydraulic model (i.e. LISFLOOD-FP) to predict water depths. The pre-trained model is then applied to simulate the flooding event that occurred in Carlisle, UK, in January 2005. The predictions are compared against the outputs produced by the calibrated LISFLOOD-FP. The performance of the CNN is also compared with a support vector regression (SVR)-based method. The results show that the CNN model outperforms SVR by a large margin. The model is highly accurate in capturing flooded cells as indicated by several quantitative assessment matrices, e.g., the estimated error for the peak flood depth is 0-0.2 meters for 97 99 real-time applications considering its simplicity, superior performance and computational efficiency.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 14

page 21

page 22

page 28

page 29

page 30

page 33

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.