Fatemehalsadat Madaeni, Karem Chokmani, Rachid Lhissou, Saeid Homayouni, Yves Gauthier, and Simon Tolszczuk-Leclerc
The Cryosphere, 16, 1447–1468, https://doi.org/10.5194/tc-16-1447-2022,https://doi.org/10.5194/tc-16-1447-2022, 2022
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.
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