A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

Xingyu Yan; Kui Xu; Wenqiang Feng; Jing Chen

doi:10.1007/s13753-021-00384-0

Volume 12 Issue 6

Dec. 2021

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Article Navigation > International Journal of Disaster Risk Science > 2021 > 12(6): 903-918

Xingyu Yan, Kui Xu, Wenqiang Feng, Jing Chen. A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches[J]. International Journal of Disaster Risk Science, 2021, 12(6): 903-918. doi: 10.1007/s13753-021-00384-0

Citation:

Xingyu Yan, Kui Xu, Wenqiang Feng, Jing Chen. A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches[J]. International Journal of Disaster Risk Science, 2021, 12(6): 903-918. doi: 10.1007/s13753-021-00384-0

Citation:

Xingyu Yan, Kui Xu, Wenqiang Feng, Jing Chen. A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches[J]. International Journal of Disaster Risk Science, 2021, 12(6): 903-918. doi: 10.1007/s13753-021-00384-0

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A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

doi: 10.1007/s13753-021-00384-0

Xingyu Yan^1,2,
Kui Xu^{1,2
,
,},
Wenqiang Feng^1,3,
Jing Chen⁴

1. State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin, 300072, China;
2. School of Civil Engineering, Tianjin University, Tianjin, 300072, China;
3. China Water Resources Beifang Investigation, Design, Research Co. Ltd., Tianjin, 300222, China;
4. Tianjin Institute of Meteorological Science, Tianjin, 300042, China

Funds:

This research was supported by the Water Pollution Control and Treatment of Major National Science and Technology Project of China (2017ZX07106001), the National Natural Science Foundation of China (51509179), and the Tianjin Natural Science Foundation (20JCQNJC01540). The authors acknowledge the assistance of the anonymous reviewers.

Available Online: 2021-12-27

Abstract

Abstract

Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.
- Flood inundation,
- Neural networks,
- Numerical simulations,
- Rapid prediction,
- Spatiotemporal prediction,
- China

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References(40)

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