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作者:

Wang, Zhuozheng (Wang, Zhuozheng.) | Chen, Bingxu (Chen, Bingxu.) | Liu, Wei (Liu, Wei.)

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EI Scopus

摘要:

In recent years, with the global climate change, intra-urban rainstorms are frequent. The backward means of urban waterlogging prevention and control do not match with the high-speed urbanization process, bringing serious waterlogging disasters to major cities in China. The current mainstream urban flooding early warning system is an integrated system integrating various information technologies with urban rainfall and flood model as the theoretical basis. However, the urban rainfall and flooding model is affected by the lack of basic data, the complicated modeling process which is not easy to implement, and the poor flexibility in analyzing the actual urban waterlogging time series characteristics. Therefore, this paper designs and implements a waterlogging early warning system based on an adaptive urban flooding model by combining the respective advantages of data-driven technology and urban rainfall model for impervious areas in cities that are prone to flooding during short-duration rainstorms. © 2023 IEEE.

关键词:

Climate change Disasters Storms Recurrent neural networks Convolutional neural networks Rain Floods Disaster prevention

作者机构:

  • [ 1 ] [Wang, Zhuozheng]Beijing University of Technology, Faculty of Information Technology, Beijing, China
  • [ 2 ] [Chen, Bingxu]Beijing University of Technology, Faculty of Information Technology, Beijing, China
  • [ 3 ] [Liu, Wei]Beijing University of Technology, Faculty of Information Technology, Beijing, China

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年份: 2023

页码: 310-317

语种: 英文

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