Imperial College London
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DrFangxinFang

Faculty of Engineering, Department of Earth Science & Engineering

Senior Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 1912f.fang

 
 
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Location

 

4.90Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cheng:2021:10.1063/5.0051213,
author = {Cheng, M and Fang, F and Navon, IM and Pain, CC},
doi = {10.1063/5.0051213},
journal = {Physics of Fluids},
pages = {1--14},
title = {A real-time flow forecasting with deep convolutional generative adversarial network: Application to flooding event in Denmark},
url = {http://dx.doi.org/10.1063/5.0051213},
volume = {33},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Real-time flood forecasting is crucial for supporting emergency responses to inundation-prone regions. Due to uncertainties in the future (e.g., meteorological conditions and model parameter inputs), it is challenging to make accurate forecasts of spatiotemporal floods. In this paper, a real-time predictive deep convolutional generative adversarial network (DCGAN) is developed for flooding forecasting. The proposed methodology consists of a two-stage process: (1) dynamic flow learning and (2) real-time forecasting. In dynamic flow learning, the deep convolutional neural networks are trained to capture the underlying flow patterns of spatiotemporal flow fields. In real-time forecasting, the DCGAN adopts a cascade predictive procedure. The last one-time step-ahead forecast from the DCGAN can act as a new input for the next time step-ahead forecast, which forms a long lead-time forecast in a recursive way. The model capability is assessed using a 100-year return period extreme flood event occurred in Greve, Denmark. The results indicate that the predictive fluid flows from the DCGAN and the high fidelity model are in a good agreement (the correlation coefficient
AU  - Cheng,M
AU  - Fang,F
AU  - Navon,IM
AU  - Pain,CC
DO  - 10.1063/5.0051213
EP  - 14
PY  - 2021///
SN  - 1070-6631
SP  - 1
TI  - A real-time flow forecasting with deep convolutional generative adversarial network: Application to flooding event in Denmark
T2  - Physics of Fluids
UR  - http://dx.doi.org/10.1063/5.0051213
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000677502900002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://pubs.aip.org/aip/pof/article/33/5/056602/1077296/A-real-time-flow-forecasting-with-deep
UR  - http://hdl.handle.net/10044/1/104958
VL  - 33
ER  -
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