08 Apr 2022
08 Apr 2022
Status: this preprint is currently under review for the journal NHESS.

Exploring the utility of social media data for urban flood impact assessment in data scarce cities

Kaihua Guo1, Mingfu Guan1, Haochen Yan1, and Faith Ka Shun Chan2 Kaihua Guo et al.
  • 1Department of Civil Engineering, the University of Hong Kong, Hong Kong, 999077, HKSAR
  • 2School of Geographical Sciences, University of Nottingham Ningbo China, Ningbo 315100, China

Abstract. The growing amount of social media data is a valuable and rapidly available information source to inform flood response and recovery. In this study, a workflow framework is developed to assess urban flood impacts by extracting and analysing social media data, as well as identifying the intensive public response areas, using the case of 2020 China Chengdu rainstorm-induced flooding. A crawler-algorithm is applied to extract and filter the social media data from the commonly used social platforms, namely Weibo (static data) and Tiktok (dynamic data). Based on the spatiotemporal analysis and the identified 232 flood sites with geological locations, the study shows that, social media activities and precipitation have a significant positive correlation temporally. The temporal evolution analysis of social media topics reveals the process of flooding enabling quickly to determine the severely affected areas. Spatially, social media data can give spatial flood information and social media activities are generally associated with the demographical distribution of users. Based on a flood simulation, the framework can generate reliable data source of urban flooding from social media, which can enhance flood risk modelling with the aid of hydrodynamic model. This study demonstrates the utility of social media data for urban flood assessment.

Kaihua Guo et al.

Status: open (extended)

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Kaihua Guo et al.


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Short summary
This study investigated the utility of social media in urban flood assessment using the case of 2020 China Chengdu flooding. We presented an efficient workflow to collect, process and identify unstructured flood related data in near real-time during a storm event. Based on identified social media database and 232 flood sites, this study shows that social media data can provide valuable spatial and timely information for urban flooding emergency management.