A deep convolutional neural network for rapid fluvial flood inundation modelling

Syed Kabir, Sandhya Patidar, Xilin Xia, Qiuhua Liang, Jeffrey Neal, Gareth Pender

Research output: Contribution to journalArticle (Academic Journal)peer-review


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% cells of the domain when 99% confidence level is drawn. The proposed method offers great potential for real-time applications considering its simplicity, superior performance and computational efficiency.
Original languageEnglish
Article number125481
JournalJournal of Hydrology
Early online date6 Sept 2020
Publication statusE-pub ahead of print - 6 Sept 2020

Bibliographical note

39 pages, 15 figures, 7 tables


  • cs.LG
  • eess.SP
  • stat.ML


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