Abstract

Increasingly frequent flood disasters have caused great losses in recent years. Urban floods induce not only natural geological disasters but also social accidents. These disaster events, called urban flood cascade compound disaster events (UFCCDEs) in this study, have significant cascade, superposition, and amplification effects. However, conventional data sources and processing methods make it difficult to analyze the detailed course of disaster events caused by urban floods, thereby hindering the vulnerability assessment of UFCCDE networks (UFCCDENs). Herein, we propose a framework considering the interactions between disaster events caused by urban floods for rapidly and comprehensively assessing the vulnerability of the UFCCDEN. First, social media data (Sina Weibo) are processed to analyze the spatio-temporal distribution of UFCCDEs and construct a UFCCDEN based on an event evolutionary graph. Second, complex network theory is applied to evaluate the importance of disaster events and the vulnerability of disaster causal chains in the constructed UFCCDEN. Finally, the global efficiency of the network is calculated to assess the propagation efficiency of the UFCCDEN before and after implementing disaster mitigation strategies based on the assessment results to demonstrate the performance of the assessment framework. The coastal megacity Guangzhou was selected as an example. The results showed that, social media data can provide detailed and valid information about UFCCDEs, which can be used to construct the UFCCDEN based on the event evolutionary graph. Waterlogging is found to be the most important disaster event in the UFCCDEN. Furthermore, power facilities, drainage facilities, and roads should be given top priority in the prevention and mitigation of urban floods because of their significant cascading amplification effects. The proposed framework can make the propagation efficiency of the UFCCDEN markedly decrease by 37–62% and 44%, based on the assessment results of disaster events and causal chains, respectively.

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