Articles | Volume 23, issue 11
Hydrol. Earth Syst. Sci., 23, 4621–4634, 2019
https://doi.org/10.5194/hess-23-4621-2019
Hydrol. Earth Syst. Sci., 23, 4621–4634, 2019
https://doi.org/10.5194/hess-23-4621-2019

Research article 15 Nov 2019

Research article | 15 Nov 2019

Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

Matthew Moy de Vitry et al.

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Interactive discussion

Status: closed
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: Publish subject to revisions (further review by editor and referees) (29 May 2019) by Thomas Kjeldsen
AR by M. Moy de Vitry on behalf of the Authors (05 Jun 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (18 Jun 2019) by Thomas Kjeldsen
RR by Anonymous Referee #2 (07 Aug 2019)
RR by Josh Myrans (24 Sep 2019)
ED: Publish subject to minor revisions (review by editor) (02 Oct 2019) by Thomas Kjeldsen
AR by M. Moy de Vitry on behalf of the Authors (03 Oct 2019)  Author's response    Manuscript
ED: Publish as is (09 Oct 2019) by Thomas Kjeldsen
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Short summary
This work demonstrates a new approach to obtain flood level trend information from surveillance footage with minimal prior information. A neural network trained to detect flood water is applied to video frames to create a qualitative flooding metric (namely, SOFI). The correlation between the real water trend and SOFI was found to be 75 % on average (based on six videos of flooding under various circumstances). SOFI could be used for flood model calibration, to increase model reliability.