Articles | Volume 16, issue 4
https://doi.org/10.5194/tc-16-1447-2022
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, Karem Chokmani, Rachid Lhissou, Saeid Homayouni​​​​​​​, Yves Gauthier, and Simon Tolszczuk-Leclerc

Viewed

Total article views: 6,153 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,201 2,864 88 6,153 93 90
  • HTML: 3,201
  • PDF: 2,864
  • XML: 88
  • Total: 6,153
  • BibTeX: 93
  • EndNote: 90
Views and downloads (calculated since 23 Jul 2021)
Cumulative views and downloads (calculated since 23 Jul 2021)

Viewed (geographical distribution)

Total article views: 6,153 (including HTML, PDF, and XML) Thereof 5,896 with geography defined and 257 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 01 Aug 2025
Download
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.
Share