Articles | Volume 21, issue 4
https://doi.org/10.5194/nhess-21-1179-2021
https://doi.org/10.5194/nhess-21-1179-2021
Research article
 | 
06 Apr 2021
Research article |  | 06 Apr 2021

Online urban-waterlogging monitoring based on a recurrent neural network for classification of microblogging text

Hui Liu, Ya Hao, Wenhao Zhang, Hanyue Zhang, Fei Gao, and Jinping Tong

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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (04 Jan 2021) by Philip Ward
AR by Ya Hao on behalf of the Authors (09 Jan 2021)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (09 Feb 2021) by Philip Ward
RR by Anonymous Referee #1 (15 Feb 2021)
RR by Valerio Lorini (24 Feb 2021)
ED: Publish subject to minor revisions (review by editor) (01 Mar 2021) by Philip Ward
AR by Ya Hao on behalf of the Authors (02 Mar 2021)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (02 Mar 2021) by Philip Ward
AR by Ya Hao on behalf of the Authors (02 Mar 2021)
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Short summary
We trained a recurrent neural network model to classify microblogging posts related to urban waterlogging and establish an online monitoring system of urban waterlogging caused by flood disasters. We manually curated more than 4400 waterlogging posts to train the RNN model so that it can precisely identify waterlogging-related posts of Sina Weibo to timely determine urban waterlogging.
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