Articles | Volume 16, issue 4
The Cryosphere, 16, 1447–1468, 2022
https://doi.org/10.5194/tc-16-1447-2022
The Cryosphere, 16, 1447–1468, 2022
https://doi.org/10.5194/tc-16-1447-2022
Research article
22 Apr 2022
Research article | 22 Apr 2022

Convolutional neural network and long short-term memory models for ice-jam predictions

Fatemehalsadat Madaeni et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2021-194', John Quilty, 14 Aug 2021
  • CC1: 'Comment on tc-2021-194', Rahim Barzegar, 22 Aug 2021
  • RC2: 'Comment on tc-2021-194', Rahim Barzegar, 24 Aug 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (26 Oct 2021) by Homa Kheyrollah Pour
AR by FATEMEHALSADAT MADAENI on behalf of the Authors (08 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (18 Dec 2021) by Homa Kheyrollah Pour
RR by Anonymous Referee #2 (18 Jan 2022)
RR by John Quilty (24 Jan 2022)
ED: Publish subject to minor revisions (review by editor) (26 Jan 2022) by Homa Kheyrollah Pour
AR by FATEMEHALSADAT MADAENI on behalf of the Authors (15 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (15 Mar 2022) by Homa Kheyrollah Pour
AR by FATEMEHALSADAT MADAENI on behalf of the Authors (23 Mar 2022)  Author's response    Manuscript
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Short summary
We developed three deep learning models (CNN, LSTM, and combined CN-LSTM networks) to predict breakup ice-jam events to be used as an early warning system of possible flooding in rivers. In the models, we used hydro-meteorological data associated with breakup ice jams. The models show excellent performance, and the main finding is that the CN-LSTM model is superior to the CNN-only and LSTM-only networks in both training and generalization accuracy.