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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Constanza S. Salvó, Randall K. Scharien, David Iqqaqsaq, Robert Briggs, Trevor Bell, and Saeid Homayouni
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In this paper, we evaluated different types of satellite radar information to locate flooded areas on sea ice. We found that changes between images over time were the most effective for identifying flooded areas, while other measurements provided information about how flooding evolves, including refreezing processes. We tested these results at a broader scale and confirmed that they can support regional monitoring. These results provide information that can support safer travel on the ice.
Danesh Shokri, Christian Larouche, and Saeid Homayouni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIX-B2-2026, 1199–1206, https://doi.org/10.5194/isprs-archives-XLIX-B2-2026-1199-2026, https://doi.org/10.5194/isprs-archives-XLIX-B2-2026-1199-2026, 2026
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EGUsphere, https://doi.org/10.5194/egusphere-2026-2457, https://doi.org/10.5194/egusphere-2026-2457, 2026
Short summary
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Ice-jam flooding poses a significant risk in cold regions, particularly for vulnerable communities. This study maps river sections most prone to ice jams based on their physical characteristics. The findings offer a practical tool for improving flood preparedness and risk management by helping decision-makers prioritize high-risk areas, especially as climate change increases the frequency of extreme winter conditions.
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F. Moradi, A. Zarei, S. Ranjbar, and S. Homayouni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W1-2022, 515–522, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-515-2023, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-515-2023, 2023
H. Rabiei-Dastjerdi, S. Mohammadi, R. Samouei, M. Kazemi, S. Matthews, G. McArdle, S. Homayouni, B. Kiani, and R. Sadeghi
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R. Sahraei, A. Ghorbanian, Y. Kanani-Sadat, S. Jamali, and S. Homayouni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W1-2022, 669–675, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-669-2023, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-669-2023, 2023
R. Sahraei, Y. Kanani-Sadat, A. Safari, and S. Homayouni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W1-2022, 677–683, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-677-2023, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-677-2023, 2023
D. Shokri, M. Zaboli, F. Dolati, and S. Homayouni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W1-2022, 721–727, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-721-2023, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-721-2023, 2023
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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...
