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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- A Contactless Breathing Pattern Recognition System Using Deep Learning and WiFi Signal D. Fan et al.
- Forecasting the River Ice Break-Up Date in the Upper Reaches of the Heilongjiang River Based on Machine Learning Z. Liu et al.
- Lanthanide molecular nanomagnets as probabilistic bits G. Gutiérrez-Finol et al.
- Physics-informed deep learning reveals climate-driven snowpack decline and threatens ecological water availability in a Californian snow-fed catchment S. Maharjan et al.
- A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics M. Abdelkader et al.
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- Real-time prediction of river ice breakup phenomena: A jittered genetic programming model and wavelet analysis integrating remotely sensed imagery and machine learning S. Andaryani & A. Afkhaminia
- Identification of the Optimal Neural Network Architecture for Prediction of Bitcoin Return T. Šestanović & T. Kalinić Milićević
- Climate and environmental data contribute to the prediction of grain commodity prices using deep learning Z. Wang et al.
- An efficient video-based rainfall intensity estimation employing different recurrent neural network models F. Rajabi et al.
- Long-term prediction of sea ice concentration with convolutional long short-term memory Ö. AK et al.
- Deep Learning for Bifurcation Detection: Extending Early Warning Signals to Dynamical Systems with Coloured Noise Y. Babazadeh Maghsoodlo et al.
- Machine Learning-Enhanced River Ice Identification in the Complex Tibetan Plateau X. Pang et al.
- Deep encoder–decoder hierarchical convolutional neural networks for conjugate heat transfer surrogate modeling T. Ebbs-Picken et al.
- Hydroclimate influences ice jam dynamics in southern Quebec watersheds through competing effects on ice cover resistance and dislocation forces L. Arsenault-Boucher et al.
- Discriminative spatial-temporal feature learning for modeling network intrusion detection systems S. Wanjau et al.
- Spatio-Temporal Coherence of mmWave/THz Channel Characteristics and Their Forecasting Using Video Frame Prediction Techniques V. Prosvirov et al.
- Intelligent and interpretable forecasting method for ice-jam flood disaster levels based on fusion model Y. Li et al.
- PS-InSAR Monitoring Integrated with a Bayesian-Optimized CNN–LSTM for Predicting Surface Subsidence in Complex Mining Goafs Under a Symmetry Perspective T. Su et al.
- Classification of remote sensing images based on multi-threshold binarization B. Rusyn et al.
- Interpretable Cotton Mapping Across Phenological Stages: Receptive-Field Enhancement and Cross-Domain Stability L. Li et al.
- Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach E. Koutsovili et al.
- Machine Learning Prediction of River Freeze-Up Dates Under Human Interventions: Insights from the Ningxia–Inner Mongolia Reach of the Yellow River L. Zhang et al.
- Impacts of spatially inconsistent permafrost degradation on streamflow in the Lena River Basin Z. Xue et al.
- Scale Effects of the Monthly Streamflow Prediction Using a State-of-the-art Deep Learning Model W. Xu et al.
- Advancements in Hydrological Modeling: The Role of bRNN-CNN-GRU in Predicting Dam Reservoir Inflow Patterns E. Abdi et al.
- 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.
- A comprehensive review of AI-based methods used for forecasting ice jam floods occurrence, severity, timing, and location A. Salimi et al.
- Flood prediction with time series data mining: Systematic review D. Hakim et al.
- Hilbert transform based combined 1D and 2D deep learning framework for power quality disturbance classification P. M & E. S
- Convolutional Neural Network-Based Tire Pressure Monitoring System Z. Márton et al.
Saved (final revised paper)
Latest update: 28 Apr 2026
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...
