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A deep convolutional neural network model for rapid prediction of fluvial flood inundation.
- Source :
-
Journal of Hydrology . Nov2020, Vol. 590, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- • A CNN model is proposed to estimate flood water depths. • The model can predict water depths for over half of a million cells instantly. • The CNN can potentially be used as a surrogate model for real-time applications. • The benefit of using CNN over other ML methods for flood modelling is presented. 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 m for the 2005 event and 0–0.5 m 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00221694
- Volume :
- 590
- Database :
- Academic Search Index
- Journal :
- Journal of Hydrology
- Publication Type :
- Academic Journal
- Accession number :
- 146811481
- Full Text :
- https://doi.org/10.1016/j.jhydrol.2020.125481