文章基本信息

标题：A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches
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作者：Xingyu Yan ; Kui Xu ; Wenqiang Feng 等
期刊名称：International Journal of Disaster Risk Science
印刷版ISSN：2095-0055
电子版ISSN：2192-6395
出版年度：2021
卷号：12
期号：6
页码：903-918
DOI：10.1007/s13753-021-00384-0
语种：English
出版社：Springer
摘要：AbstractClimate 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.
关键词：KeywordsenFlood inundationNeural networksNumerical simulationsRapid predictionSpatiotemporal predictionChina