Preprints
https://doi.org/10.5194/nhess-2020-335
https://doi.org/10.5194/nhess-2020-335

  16 Nov 2020

16 Nov 2020

Review status: a revised version of this preprint was accepted for the journal NHESS and is expected to appear here in due course.

Online Urban Waterlogging Monitoring Based on Recurrent Neural Network for Classification of Microblogging Text

Hui Liu, Ya Hao, Wenhao Zhang, Hanyue Zhang, Fei Gao, and Jinping Tong Hui Liu et al.
  • Business School, Changzhou University, Changzhou, 213159, China

Abstract. With the global climate change and rapid urbanization, urban flood disaster spreads and becomes increasingly serious in China. The urban rainstorm and waterlogging have become an urgent challenge that needs to be real-time monitored and further predicted for the improvement of urbanization construction. In this paper, we trained a recurrent neural network (RNN) model to classify microblogging posts related to urban waterlogging, and establish an online monitoring system of urban waterlogging caused by flood disaster. We manually curated more than 4,400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely find out urban waterlogging. The RNN model has been thoroughly evaluated, and our experimental results showed that it achieved higher accuracy than traditional machine learning methods, such as SVM and GBDT. Furthermore, we build a nationwide map of urban waterlogging based on recent two-year microblogging data.

Hui Liu et al.

 
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Status: closed
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Hui Liu et al.

Hui Liu et al.

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