Constructing Scenario Dimension Model of City Waterlogging Under Big Data Environment

  Objectives  Under big data environment, high frequency and real-time data collection, multi-sector interaction and data sharing, cross-border data fusion and multi-granularity data scaling of waterlogging scenario description have all become possible. Accordingly, in the representation of waterlog scenarios, it is very necessary for emergency managers to recognize the overall relevance of the scenario elements from a full-view perspective.

  Methods  Based on full-view management theory, we firstly propose a multi-dimension scenario framework of waterlogging disasters, with three dimensions selected, i.e. scenario level, scenario type and scenario granularity. Next, based on data nesting theory, focusing on the relationship of scenario elements at three typical scenario levels:city, community and residents, the scenario nesting structures are proposed, and the scenario dimension model of waterlogging disasters is constructed. Furthermore, an iterative algorithm for multi-dimension scenario generation is proposed, which integrates multi-level waterlogging scenario data and realizes the modification and improvement of scenario description. Finally, through a case study in Donghu, Wuhan City, the reasonability and validity of the proposed model are verified.

  Results  The case results show that through the various generic scenario elements and their associations provided by the proposed scenario dimension model, the scenario information of various administrative levels can be generated for effective scenario recognition and waterlogging response. Compared with traditional scenario representation, the proposed model shows advantages in multi-level scenario representation, dynamic scenario element nesting and multi-party interaction.

  Conclusions  The waterlogging scenarios of different levels, types and granularities interact with each other. Through the establishment of scenario dimension model, those scenario elements can be associated, which is greatly helpful for understanding waterlogging scenario information and makes the emergency response more effective.