Articles | Volume 22, issue 10
Nat. Hazards Earth Syst. Sci., 22, 3349–3359, 2022
https://doi.org/10.5194/nhess-22-3349-2022

Special issue: Advances in flood forecasting and early warning

Nat. Hazards Earth Syst. Sci., 22, 3349–3359, 2022
https://doi.org/10.5194/nhess-22-3349-2022
Research article
17 Oct 2022
Research article | 17 Oct 2022

Real-time urban rainstorm and waterlogging disaster detection by Weibo users

Haoran Zhu et al.

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Cited articles

Avvenuti, M., Del Vigna, F., Cresci, S., Marchetti, A., and Tesconi, M.: Pulling information from social media in the aftermath of unpredictable disasters, in: 2015 2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), 258–264, Rennes, France, 30 November–2 December 2015, IEEE, https://doi.org/10.1109/ict-dm.2015.7402058, 2015. 
Beijing Daily: Beijing lifts rainstorm warning, https://weibo.com/6215401356/JfG8swIOQ (last access: 29 August 2022), 2022. 
Bisht, D., Chatterjee, C., Kalakoti, S., Upadhyay, P., Sahoo, M., and Panda, A.: Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study, Nat. Hazards, 84, 749–776, https://doi.org/10.1007/s11069-016-2455-1, 2016. 
Bo, T.: Application of earthquake disaster data mining and intensity rapid assessment based on social media, Institute of Engineering Mechanics, China Earthquake Administration, https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDLAST2022andfilename=1019239057.nh (last access: 29 August 2022), 2018. 
Cao, Y. B., Wu, Y. M., and Xu, R. J.: Research about the Perceptible Area Extracted after the Earthquake Based on the Microblog Public Opinion, J. Seismol. Res., 40, 303–310, 2017. 
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Short summary
We promote a new method to detect waterlogging disasters. Residents are directly affected by waterlogging, and we can collect their comments on social networks. Compared to official-authentication and personal-certification users, the microblogs posted by general users can better show the intensity and timing of waterlogging. Through text and sentiment features, we can separate microblogs with waterlogging information from other ones and mark high-risk regions on maps.
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