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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- Classification of remote sensing images based on multi-threshold binarization B. Rusyn et al. 10.15407/vidbir2023.51.062
- Convolutional Neural Network-Based Tire Pressure Monitoring System Z. Márton et al. 10.1109/ACCESS.2023.3294408
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- Modelling the effect of cascade reservoir regulation on ice-jam flooding M. Liu et al. 10.1016/j.jhydrol.2024.131358
Latest update: 20 Nov 2024
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...
