Articles | Volume 16, issue 4
The Cryosphere, 16, 1447–1468, 2022
The Cryosphere, 16, 1447–1468, 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.

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Cited articles

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Barnes-Svarney, P. L. and Montz, B. E.: An ice jam prediction model as a tool in floodplain management, Water Resour. Res., 21, 256–260, 1985. 
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.