Articles | Volume 16, issue 4
https://doi.org/10.5194/tc-16-1447-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-1447-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Apr 2022
Research article |
| 22 Apr 2022
| 22 Apr 2022
Convolutional neural network and long short-term memory models for ice-jam predictions
Fatemehalsadat Madaeni
CORRESPONDING AUTHOR
INRS-ETE, Université du Québec, Québec City, G1K 9A9,
Canada
Karem Chokmani
INRS-ETE, Université du Québec, Québec City, G1K 9A9,
Canada
Rachid Lhissou
INRS-ETE, Université du Québec, Québec City, G1K 9A9,
Canada
Saeid Homayouni
INRS-ETE, Université du Québec, Québec City, G1K 9A9,
Canada
Yves Gauthier
INRS-ETE, Université du Québec, Québec City, G1K 9A9,
Canada
Simon Tolszczuk-Leclerc
EMGeo Operations, Natural Resources Canada, Ottawa, K1S 5K2, Canada
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- Features extraction from multi-spectral remote sensing images based on multi-threshold binarization B. Rusyn et al. https://doi.org/10.1038/s41598-023-46785-7
- Multi-model prediction of ice jam occurrence probability at Toudaoguai section of the Yellow River based on ice thickness simulation with Unified Degree-Day Method H. Hou et al. https://doi.org/10.1016/j.ejrh.2026.103486
- Real-time prediction of river ice breakup phenomena: A jittered genetic programming model and wavelet analysis integrating remotely sensed imagery and machine learning S. Andaryani & A. Afkhaminia https://doi.org/10.1016/j.jhydrol.2024.132097
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- Climate and environmental data contribute to the prediction of grain commodity prices using deep learning Z. Wang et al. https://doi.org/10.1002/sae2.12041
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- Hydroclimate influences ice jam dynamics in southern Quebec watersheds through competing effects on ice cover resistance and dislocation forces L. Arsenault-Boucher et al. https://doi.org/10.1007/s11069-024-07078-y
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Saved (final revised paper)
Latest update: 17 Jun 2026
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.
We developed three deep learning models (CNN, LSTM, and combined CN-LSTM networks) to predict...
