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A deep convolutional neural network model for rapid prediction of fluvial flood inundation

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journal contribution
posted on 11.09.2020, 09:05 by Syed Kabir, Sandhya Patidar, Xilin XiaXilin Xia, Qiuhua LiangQiuhua Liang, Jeffrey Neal, Gareth Pender
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 predicting 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.

Funding

This work is partly funded by the Newton Fund and UK Met Office ‘WCSSP-India Lot 7: Building a Flood Hazard Impact Model for India (FHIM-India) (DN394978)’ project.

History

School

  • Architecture, Building and Civil Engineering

Published in

Journal of Hydrology

Volume

590

Publisher

Elsevier

Version

AM (Accepted Manuscript)

Rights holder

© Elsevier

Publisher statement

This paper was accepted for publication in the journal Journal of Hydrology and the definitive published version is available at https://doi.org/10.1016/j.jhydrol.2020.125481.

Acceptance date

26/08/2020

Publication date

2020-09-06

Copyright date

2020

ISSN

0022-1694

Language

en

Depositor

Dr Xilin Xia. Deposit date: 9 September 2020

Article number

125481