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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- An efficient video-based rainfall intensity estimation employing different recurrent neural network models F. Rajabi et al. 10.1007/s12145-024-01290-x
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- Deep encoder–decoder hierarchical convolutional neural networks for conjugate heat transfer surrogate modeling T. Ebbs-Picken et al. 10.1016/j.apenergy.2024.123723
- Hydroclimate influences ice jam dynamics in southern Quebec watersheds through competing effects on ice cover resistance and dislocation forces L. Arsenault-Boucher et al. 10.1007/s11069-024-07078-y
- Discriminative spatial-temporal feature learning for modeling network intrusion detection systems S. Wanjau et al. 10.3233/JCS-220031
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- Classification of remote sensing images based on multi-threshold binarization B. Rusyn et al. 10.15407/vidbir2023.51.062
- Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach E. Koutsovili et al. 10.3390/ijgi12110464
- Impacts of spatially inconsistent permafrost degradation on streamflow in the Lena River Basin Z. Xue et al. 10.1007/s11431-023-2757-2
- Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model W. Xu et al. 10.1007/s11269-022-03216-y
- Advancements in Hydrological Modeling: The Role of bRNN-CNN-GRU in Predicting Dam Reservoir Inflow Patterns E. Abdi et al. 10.3390/w17111660
- Forecasting Multi-Step-Ahead Street-Scale Nuisance Flooding using a seq2seq LSTM Surrogate Model for Real-Time Application in a Coastal-Urban City B. Roy et al. 10.1016/j.jhydrol.2025.132697
- A comprehensive review of AI-based methods used for forecasting ice jam floods occurrence, severity, timing, and location A. Salimi et al. 10.1016/j.coldregions.2024.104305
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- Hilbert transform based combined 1D and 2D deep learning framework for power quality disturbance classification P. M & E. S 10.1016/j.epsr.2025.112031
- 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: 22 Aug 2025
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...
