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

International Journal of Disaster Risk Science

Wenqiang Feng¹  Xingyu Yan²  Kui Xu²  Jing Chen³ 

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

关键词: Flood inundation;    Neural networks;    Numerical simulations;    Rapid prediction;    Spatiotemporal prediction;    China;   

DOI  :  10.1007/s13753-021-00384-0

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY