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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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
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Noumonvi Yawu Sena, Karem Chokmani, Erwan Gloaguen, and Monique Bernier
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-316, https://doi.org/10.5194/tc-2020-316, 2020
Manuscript not accepted for further review
Sophie Dufour-Beauséjour, Anna Wendleder, Yves Gauthier, Monique Bernier, Jimmy Poulin, Véronique Gilbert, Juupi Tuniq, Amélie Rouleau, and Achim Roth
The Cryosphere, 14, 1595–1609, https://doi.org/10.5194/tc-14-1595-2020, https://doi.org/10.5194/tc-14-1595-2020, 2020
Short summary
Short summary
Inuit have reported greater variability in seasonal sea ice conditions. For Deception Bay (Nunavik), an area prized for seal and caribou hunting, an increase in snow precipitation and a shorter snow cover period is expected in the near future. In this context, and considering ice-breaking transport in the fjord by mining companies, we combined satellite images and time-lapse photography to monitor sea ice in the area between 2015 and 2018.
O. Reisi Gahrouei, S. Homayouni, and A. Safari
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M. Saadat, M. Hasanlou, and S. Homayouni
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L. Yousefizadeh, R. Shahhoseini, and S. Homayouni
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R. Farhadiani, S. Homayouni, and A. Safari
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W18, 379–385, https://doi.org/10.5194/isprs-archives-XLII-4-W18-379-2019, https://doi.org/10.5194/isprs-archives-XLII-4-W18-379-2019, 2019
E. Kiana, S. Homayouni, M. A. Sharifi, and M. R. Farid-Rohani
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Charles Gignac, Monique Bernier, and Karem Chokmani
The Cryosphere, 13, 451–468, https://doi.org/10.5194/tc-13-451-2019, https://doi.org/10.5194/tc-13-451-2019, 2019
Short summary
Short summary
The IcePAC tool is made to estimate the probabilities of specific sea ice conditions based on historical sea ice concentration time series from the EUMETSAT OSI-409 product (12.5 km grid), modelled using the beta distribution and used to build event probability maps, which have been unavailable until now. Compared to the Canadian ice service atlas, IcePAC showed promising results in the Hudson Bay, paving the way for its usage in other regions of the cryosphere to inform stakeholders' decisions.
S. Niazmardi, A. Safari, and S. Homayouni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W4, 201–207, https://doi.org/10.5194/isprs-archives-XLII-4-W4-201-2017, https://doi.org/10.5194/isprs-archives-XLII-4-W4-201-2017, 2017
A. Alizadeh Naeini, M. Babadi, and S. Homayouni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W4, 27–30, https://doi.org/10.5194/isprs-archives-XLII-4-W4-27-2017, https://doi.org/10.5194/isprs-archives-XLII-4-W4-27-2017, 2017
N. Jamshidpour, S. Homayouni, and A. Safari
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W4, 91–96, https://doi.org/10.5194/isprs-archives-XLII-4-W4-91-2017, https://doi.org/10.5194/isprs-archives-XLII-4-W4-91-2017, 2017
Stéphane De Munck, Yves Gauthier, Monique Bernier, Karem Chokmani, and Serge Légaré
Nat. Hazards Earth Syst. Sci., 17, 1033–1045, https://doi.org/10.5194/nhess-17-1033-2017, https://doi.org/10.5194/nhess-17-1033-2017, 2017
Short summary
Short summary
Ice jams emerge from the accumulation of fragmented ice on a specific section of a river, obstructing the channel and restricting the flow. The resulting floods are socioeconomically costly as well as life threatening. When breakup occurs and ice starts to move downstream the river, a key question is, where would the released ice be susceptible to jam? The goal of this work was to develop a simplified geospatial model to estimate the predisposition of a river channel to ice jams.
Related subject area
Discipline: Other | Subject: Natural Hazards
The 2020 glacial lake outburst flood at Jinwuco, Tibet: causes, impacts, and implications for hazard and risk assessment
Guoxiong Zheng, Martin Mergili, Adam Emmer, Simon Allen, Anming Bao, Hao Guo, and Markus Stoffel
The Cryosphere, 15, 3159–3180, https://doi.org/10.5194/tc-15-3159-2021, https://doi.org/10.5194/tc-15-3159-2021, 2021
Short summary
Short summary
This paper reports on a recent glacial lake outburst flood (GLOF) event that occurred on 26 June 2020 in Tibet, China. We find that this event was triggered by a debris landslide from a steep lateral moraine. As the relationship between the long-term evolution of the lake and its likely landslide trigger revealed by a time series of satellite images, this case provides strong evidence that it can be plausibly linked to anthropogenic climate change.
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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...
