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• W3216719782 abstract "Abstract The emergence of big data is breaking the spatial and time limitations of urban waterlogging scenario description. The scenario data of different dimensions (e.g., administrative levels, sectors, granularities, and time) have become highly integrated. Accordingly, a structural and systematic model is needed to represent waterlogging scenarios for more efficient waterlogging response decision-making. In this article, a full-view urban waterlogging scenario is first defined and described from four dimensions. Next a structured representation of scenario element is given based on knowledge unit method. The full-view scenario model is then constructed by extracting the scenario correlation structures between different dimensions (called scenario nesting), i.e., inheritance nesting, feedback nesting, aggregation nesting, and selection nesting. Finally, a real-world case study in Wuhan East Lake High-tech Development Zone, China is evaluated to verify the reasonability of the full-view model. The results show that the proposed model effectively integrates scenario data from different dimensions, which helps generate the complete key scenario information for urban waterlogging decision-making. The full-view scenario model is expected to be applicable for other disasters under big data environment." @default.
• W3216719782 created "2021-12-06" @default.
• W3216719782 creator A5047560285 @default.
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• W3216719782 creator A5079213964 @default.
• W3216719782 date "2021-01-01" @default.
• W3216719782 modified "2023-10-14" @default.
• W3216719782 title "A full-view scenario model for urban waterlogging response in a big data environment" @default.
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• W3216719782 doi "https://doi.org/10.1515/geo-2020-0317" @default.
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