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.

Viewed

Total article views: 1,336 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
861 443 32 1,336 22 20
  • HTML: 861
  • PDF: 443
  • XML: 32
  • Total: 1,336
  • BibTeX: 22
  • EndNote: 20
Views and downloads (calculated since 23 Jul 2021)
Cumulative views and downloads (calculated since 23 Jul 2021)

Viewed (geographical distribution)

Total article views: 1,233 (including HTML, PDF, and XML) Thereof 1,233 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 05 Jul 2022
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.