Abstract
Abstract Planning to evaluate flood disaster vulnerability is a crucial step towards risk mitigation and adaptation. In this study, the vulnerability curve model was established with one highly popular area of research in mind: big data. Web crawler technology was used to extract text information related to floods from Internet and social media platforms. Based on the three indicators of rainfall intensity, duration and coverage area, the heavy rainfall index was calculated, while the comprehensive disaster index was calculated based on the affected population, area and direct economic loss. Taking the heavy rainfall index as an independent variable and comprehensive disaster index as a dependent variable, the vulnerability curve of flood disasters was established, and the performance of this model was validated by comparing it with real-life situations. The results show that the relationship between rainfall and disaster is significant, and there is exponential correlation between the heavy rainfall index and comprehensive disaster index. This model is more than 65% accurate, which demonstrates the discriminative power of the established curve model. The results provide some basis for flood control and management in cities.
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