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
Related authors
No articles found.
Motahareh Esfandyari Kaloukan, Shirin Malihi, Siavash Iran-Pour, Danesh Shokri, and Saeid Homayouni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., XI-3-2026, 625–632, https://doi.org/10.5194/isprs-annals-XI-3-2026-625-2026, https://doi.org/10.5194/isprs-annals-XI-3-2026-625-2026, 2026
Aydin Ebrahimi, Ali Mohammadzadeh, Armin Moghimi, and Saeed Homayouni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W22-2025, 21–27, https://doi.org/10.5194/isprs-archives-XLVIII-4-W22-2025-21-2026, https://doi.org/10.5194/isprs-archives-XLVIII-4-W22-2025-21-2026, 2026
Homayoon Zahmatkesh, Rahim Ali Abbaspour, Abbas Abedini, and Saeid Homayouni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W8-2025, 835–840, https://doi.org/10.5194/isprs-annals-X-4-W8-2025-835-2026, https://doi.org/10.5194/isprs-annals-X-4-W8-2025-835-2026, 2026
Benyamin Hosseiny, Mahdieh Zaboli, and Saeid Homayouni
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-2024, 163–168, https://doi.org/10.5194/isprs-annals-X-4-2024-163-2024, https://doi.org/10.5194/isprs-annals-X-4-2024-163-2024, 2024
Ramin Farhadiani and Saeid Homayouni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-4-2024, 17–23, https://doi.org/10.5194/isprs-archives-XLVIII-M-4-2024-17-2024, https://doi.org/10.5194/isprs-archives-XLVIII-M-4-2024-17-2024, 2024
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
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W1-2022, 623–630, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-623-2023, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-623-2023, 2023
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
Cited articles
Alipanahi, B., Delong, A., Weirauch, M. T., and Frey, B. J.: Predicting the
sequence specificities of DNA-and RNA-binding proteins by deep
learning, Nat. Biotechnol., 33, 831–838, 2015.
Althoff, D., Rodrigues, L. N., and Bazame, H. C.: Uncertainty
quantification for hydrological models based on neural networks: the dropout
ensemble, Stoch. Env. Res. Risk A., 35,
1051–1067, 2021.
Anaconda Software Distribution: Anaconda Documentation, Version 2-2.4, https://docs.anaconda.com/ (last access: 10 February 2022), 2016.
Apaydin, H., Feizi, H., Sattari, M. T., Colak, M. S., Shamshirband, S., and
Chau, K. W.: Comparative analysis of recurrent neural network architectures
for reservoir inflow forecasting, Water, 12, 1500, https://doi.org/10.3390/w12051500, 2020.
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.
Barzegar, R., Aalami, M. T., and Adamowski, J.: Short-term water quality
variable prediction using a hybrid CNN–LSTM deep learning model, Stoch,
Env. Res. Risk A., 34, 415–433, https://doi.org/10.1007/s00477-020-01776-2, 2020.
Barzegar, R., Aalami, M. T., and Adamowski, J.: Coupling a hybrid CNN-LSTM
deep learning model with a Boundary Corrected Maximal Overlap Discrete
Wavelet Transform for multiscale Lake water level forecasting, J.
Hydrol., 598, 126196, https://doi.org/10.1016/j.jhydrol.2021.126196, 2021.
Beltaos, S.: Numerical computation of river ice jams, Can. J.
Civil Eng., 20, 88–99, 1993.
Bengio, Y.: Practical recommendations for gradient-based training of deep
architectures, Neural networks: Tricks of the trade, 437–478, 2012.
Bergstra, J. and Bengio, Y.: Random search for hyper-parameter
optimization, J. Mach. Learn. Res., 13, 281–305, 2012.
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D, and Bengio, Y.: Theano: A CPU and GPU math compiler, in:
Python. Proc. 9th Python in Science Conf., Austin, Texas, 3–10, https://doi.org/10.25080/Majora-92bf1922-003, June 28–July 3 2010.
Brownlee, J.: Long short-term memory networks with python: develop sequence
prediction models with deep learning, Machine Learning Mastery, EBook, 2017.
Brownlee, J.: Better deep learning: train faster, reduce overfitting, and
make better predictions, Machine Learning Mastery, EBook, 2018a.
Brownlee, J.: Deep learning for time series forecasting: predict the future
with MLPs, CNNs and LSTMs in Python, Machine Learning Mastery, EBook, 2018b.
Brownlee, J.: A Gentle Introduction to Exploding Gradients in Neural Networks, Machine Learning Mastery, https://machinelearningmastery.com/exploding-gradients-in-neural-networks/,
last access: 12 February 2022.
Brunel, A., Pasquet, J., Rodriguez, N., Comby, F., Fouchez, D.,
and Chaumont, M.: A CNN adapted to time series for the classification of
Supernovae, Electronic Imaging, 2019, 14, 1–8, 2019.
Brunello, A., Marzano, E., Montanari, A., and Sciavicco, G.: J48SS: A novel
decision tree approach for the handling of sequential and time series
data, Computers, 8, 1–28, https://doi.org/10.3390/computers8010021, 2019.
Brunner, G. W.: Hec-ras (river analysis system), North American Water and
Environment Congress & Destructive Water, 3782–3787, 2002.
Carson, R., Beltaos, S., Groeneveld, J., Healy, D., She, Y., Malenchak, J.,
Morris, M., Saucet, J. P., Kolerski, T., and Shen, H. T.: Comparative testing of numerical models of river ice
jams, Can. J. Civil Eng., 38, 669–678, 2011.
Carson, R. W., Beltaos, S., Healy, D., and Groeneveld, J.: Tests of river
ice jam models – phase 2, in: Proceedings of the 12th Workshop on the Hydraulics
of Ice Covered Rivers, Edmonton, Alta,Edmonton, AB, 19–20, 19–20 June 2003.
Chen, R., Wang, X., Zhang, W., Zhu, X., Li, A., and Yang, C.: A hybrid
CNN-LSTM model for typhoon formation forecasting, GeoInformatica, 23,
375–396, 2019.
Cui, Z., Chen, W., and Chen, Y.: Multi-scale convolutional neural networks
for time series classification, arXiv [preprint], arXiv:1603.06995, 11 May 2016.
De Coste, M., Li, Z., Pupek, D., and Sun, W.: A hybrid ensemble modelling
framework for the prediction of breakup ice jams on Northern Canadian
Rivers, Cold Reg. Sci. Technol., 189, 103302, https://doi.org/10.1016/j.coldregions.2021.103302, 2021.
del Campo, F. A., Neri, M. C. G., Villegas, O. O. V., Sánchez, V. G. C.,
Domínguez, H. D. J. O., and Jiménez, V. G.: Auto-adaptive
multilayer perceptron for univariate time series classification, Expert
Syst. Appl., 181, 115147, https://doi.org/10.1016/j.eswa.2021.115147, 2021.
Devineau, G., Moutarde, F., Xi, W., and Yang, J.: Deep learning for hand
gesture recognition on skeletal data, in: 13th IEEE International Conference on
Automatic Face & Gesture Recognition, 106–113, https://doi.org/10.1109/FG.2018.00025, 15–19 May 2018a.
Devineau, G., Xi, W., Moutarde, F., and Yang, J.: Convolutional neural
networks for multivariate time series classification using both inter-and
intra-channel parallel convolutions, Reconnaissance des Formes, Image,
Apprentissage et Perception, RFIAP'2018, 2018b.
Dieleman, S., Schlüter, J., Raffel, C., Olson, E., Sønderby, S. K.,
Nouri, D., Maturana, D., Thoma, M., Battenberg, E., Kelly, J., De Fauw, J., Heilman, M., diogo149; McFee, B., Weideman, H., takacsg84, peterderivaz, Jon, instagibbs, Rasul, K., CongLiu, Britefury, and Degrave, J.: Lasagne: First release, (Version
v0.1), Zenodo [data set], https://doi.org/10.5281/zenodo.27878, 2015.
Données Québec: Historique (publique) d'embâcles
répertoriés au MSP – Données Québec, https://www.donneesquebec.ca/recherche/dataset/historique-publique-d-embacles-repertories-au-msp,
last access: 15 June 2021.
Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P. A.:
Deep neural network ensembles for time series classification, in: International
Joint Conference on Neural Networks, Budapest, Hungry, 1–6, https://doi.org/10.48550/arXiv.1903.06602, 14 July 2019a.
Fawaz, H. I., Forestier, G., Weber, J., Idoumghar, L., and Muller, P. A.:
Deep learning for time series classification: a review, Data Min.
Knowl. Disc., 33, 917–963, 2019b.
Fischer, T. and Krauss, C.: Deep learning with long short-term memory
networks for financial market predictions, Eur. J. Oper.
Res., 270, 654–669, 2018.
Gamboa, J. C. B.: Deep learning for time-series analysis, arXiv [preprint], arXiv:1701.01887, 7 January 2017.
Gao, Y., Hendricks, L. A., Kuchenbecker, K. J., and Darrell, T.: Deep
learning for tactile understanding from visual and haptic data,
in: International Conference on Robotics and Automation, 536–543, https://doi.org/10.48550/arXiv.1511.06065, 16–21 May 2016.
Garbin, C., Zhu, X., and Marques, O.: Dropout vs. batch normalization: an
empirical study of their impact to deep learning, Multimed. Tools
Appl., 79, 12777–12815, https://doi.org/10.1007/s11042-019-08453-9, 2020.
Glorot, X. and Bengio, Y.: Understanding the difficulty of training deep
feedforward neural networks, in: Proceedings of the Thirteenth International
Conference on Artificial Intelligence and Statistics, Sardinia, Italy, Chia Laguna Resort, 249–256, 13–15 May 2010.
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y.: Deep
learning, vol. 1, no. 2, MIT press, Cambridge, ISBN 978-0-262-03561-3 2016.
Government of Canada: The Atlas of Canada – Toporama, Natural Resources Canada, https://atlas.gc.ca/toporama/en/index.html, last access: 15 March 2020.
Government of Canada: National Hydro Network – NHN – GeoBase Series, Natural Resources Canada, https://open.canada.ca/data/en/dataset/a4b190fe-e090-4e6d-881e-b87956c07977,
last access: 15 June 2021a.
Government of Canada: National Hydrographic Network, Natural Resources Canada,
https://www.nrcan.gc.ca/science-and-data/science-and-research/earth-sciences/geography/topographic-information/geobase-surface-water-program-geeau/national-hydrographic-network/21361,
last access: 10 April 2021b.
Graf, R., Kolerski, T., and Zhu, S.: Predicting Ice Phenomena in a River
Using the Artificial Neural Network and Extreme Gradient
Boosting, Resources, 11, 12 pp., https://doi.org/10.3390/resources11020012, 2022.
Gu, J.,Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., and Chen, T.: Recent advances in convolutional neural networks, Pattern
Recogn., 77, 354–377, 2018.
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., J. Smith, N., Kern, R., Picus, M., Hoyer, S., H. van Kerkwijk, M., Brett, M., Haldane, A., Fernández del Río, J., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, Kevin., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E.: Array programming with
NumPy, Nature, 585, 357–362, 2020.
Hatami, N., Gavet, Y., and Debayle, J.: Classification of time-series
images using deep convolutional neural networks, in: Tenth International
Conference on Machine Vision, Vienna, Austria, https://doi.org/10.48550/arXiv.1710.00886, 13–15 November 2017, 2018.
He, K., Zhang, X., Ren, S., and Sun, J.: Delving deep into rectifiers:
Surpassing human-level performance on imagenet classification, in: Proceedings
of the IEEE International Conference on Computer Vision, Santiago, Chile, 1026–1034, https://doi.org/10.48550/arXiv.1502.01852, 7–13 December 2015.
Hunter, J. D.: Matplotlib: A 2D graphics environment, IEEE Ann.
Hist. Comput., 9, 90–95, 2007.
Ioffe, S. and Szegedy, C.: Batch normalization: Accelerating deep network
training by reducing internal covariate shift, in: International Conference on
Machine Learning, Lille, France, 448–456, https://doi.org/10.48550/arXiv.1502.03167, 6–11 July 2015.
Jordan, J.: Setting the learning rate of your neural network, https://www.jeremyjordan.me/nn-learning-rate/, last access: 3 February
2020.
Jović, A., Brkić, K., and Bogunović, N.: Decision tree
ensembles in biomedical time-series classification, in: Joint DAGM (German
Association for Pattern Recognition) and OAGM Symposium, Graz, Austria, 408–417, https://doi.org/10.1007/978-3-642-32717-9_41, 29–31 August 2012.
Jozefowicz, R., Zaremba, W., and Sutskever, I.: An empirical exploration of
recurrent network architectures, in: International Conference on Machine
Learning, Lille, France, 2342–2350, 6–11 July 2015.
Karim, F., Majumdar, S., Darabi, H., and Chen, S.: LSTM fully convolutional
networks for time series classification, IEEE Access, 6, 1662–1669, 2017.
Karim, F., Majumdar, S., and Darabi, H.: Insights into LSTM fully
convolutional networks for time series classification, IEEE Access, 7,
67718–67725, 2019a.
Karim, F., Majumdar, S., Darabi, H., and Harford, S.: Multivariate
lstm-fcns for time series classification, Neural Networks, 116, 237–245,
2019b.
Kashiparekh, K., Narwariya, J., Malhotra, P., Vig, L., and Shroff, G.:
ConvTimeNet: A pre-trained deep convolutional neural network for time series
classification, in: International Joint Conference on Neural Networks, Budapest, Hungary, 1–8, https://doi.org/10.48550/arXiv.1904.12546, 14–19 July
2019.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.
Li, D., Djulovic, A., and Xu, J. F.: A Study of kNN using ICU multivariate
time series data, in: In Proceedings of the International Conference on Data Science (ICDATA), p. 1, The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), edited by: Stahlbock, R. and
Weiss, G. M., 211–217, 2013.
Li, X., Zhang, Y., Zhang, J., Chen, S., Marsic, I., Farneth, R. A., and
Burd, R. S.: Concurrent activity recognition with multimodal CNN-LSTM
structure, arXiv [preprint], arXiv:1702.01638, 6 February 2017.
Lin, J., Williamson, S., Borne, K., and DeBarr, D.: Pattern recognition in
time series, Advances in Machine Learning and Data Mining for Astronomy, 1,
617–645, 2012.
Lindenschmidt, K. E.: RIVICE – a non-proprietary, open-source,
one-dimensional river-ice model, Water, 9, 314, https://doi.org/10.3390/w9050314, 2017.
Lipton, Z. C., Berkowitz, J., and Elkan, C.: A critical review of recurrent
neural networks for sequence learning, arXiv [preprint], arXiv:1506.00019, 29 May
2015.
Livieris, I. E., Pintelas, E., and Pintelas, P.: A CNN–LSTM model for gold
price time-series forecasting, Neural Comput. Appl., 32,
17351–17360, 2020.
Lu, N., Wu, Y., Feng, L., and Song, J.: Deep learning for fall detection:
Three-dimensional CNN combined with LSTM on video kinematic data, IEEE
J. Biomed. Health, 23, 314–323, 2018.
Luan, Y. and Lin, S.: Research on text classification based on CNN and
LSTM, in: International Conference on Artificial Intelligence and Computer
Applications, Dalian, China, 352–355, https://doi.org/10.1109/ICAICA.2019.8873454, 29–31 March 2019.
Madaeni, F., Lhissou, R., Chokmani, K., Raymond, S., and Gauthier, Y.: Ice
jam formation, breakup and prediction methods based on hydroclimatic data
using artificial intelligence: A review, Cold Reg. Sci.
Technol., 174, 103032, https://doi.org/10.1016/j.coldregions.2020.103032, 2020.
Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P.: High-resolution
aerial image labeling with convolutional neural networks, IEEE T.
Geosci. Remote,55, 7092–7103, 2017.
Mahabir, C., Hicks, F., and Fayek, A. R.: Neuro-fuzzy river ice breakup
forecasting system, Cold Reg. Sci. Technol., 46, 100–112,
2006.
Mahabir, C., Hicks, F. E., and Fayek, A. R.: Transferability of a
neuro-fuzzy river ice jam flood forecasting model, Cold Reg. Sci.
Technol.,48, 188–201, 2007.
Mahfouf, J. F., Brasnett, B., and Gagnon, S.: A Canadian precipitation
analysis (CaPA) project: Description and preliminary
results, Atmos. Ocean, 45, 1–17, https://doi.org/10.3137/ao.v450101, 2007.
Massie, D. D., White, K. D., and Daly, S. F.: Application of neural networks to
predict ice jam occurrence, Cold Reg. Sci. Technol., 35,
115–122, 2002.
Masters, D. and Luschi, C.: Revisiting small batch training for deep
neural networks, arXiv [preprint], arXiv:1804.07612, 20 April 2018.
Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C.,
Ebisuzaki, W., Jovic, D., Woollen, J., Mitchell, K., Rogers, E., Ek, M., Fan, Y., Grumbine, R., Higgins, W., Li, H., Lin, Y., Manikin, G., Parrish, D., and Shi, W.: North American regional
reanalysis, B. Am. Meteorol. Soc., 87, 343–360,
2006.
Montavon, G., Samek, W., and Müller, K. R.: Methods for interpreting
and understanding deep neural networks, Digit. Signal Process., 73, 1–15, https://doi.org/10.1016/j.dsp.2017.10.011, 2018.
Mutegeki, R. and Han, D. S.: A CNN-LSTM approach to human activity
recognition, in: International Conference on Artificial Intelligence in
Information and Communication, Fukuoka, Japan, 362–366, https://doi.org/10.1109/ICAIIC48513.2020.9065078, 19–21 February 2020.
Nanopoulos, A., Alcock, R., and Manolopoulos, Y.: Feature-based
classification of time-series data, International Journal of Computer
Research, 10, 49–61, 2001.
Nosratabadi, S., Mosavi, A., Duan, P., Ghamisi, P., Filip, F., Band,
S. S., Reuter, U., Gama, J., and Gandomi, A. H.: Data science in economics: comprehensive review of
advanced machine learning and deep learning methods, Mathematics, 8, 1–25, 2020.
Oh, S. L., Ng, E. Y., San Tan, R., and Acharya, U. R.: Automated diagnosis
of arrhythmia using combination of CNN and LSTM techniques with variable
length heart beats, Comput. Biol. Med., 102, 278–287, 2018.
Ombabi, A. H., Ouarda, W., and Alimi, A. M.: Deep learning CNN–LSTM
framework for Arabic sentiment analysis using textual information shared in
social networks, Social Network Analysis and Mining, 10, 1–13, https://doi.org/https://doi.org/10.1007/s13278-020-00668-1, 2020.
Ordóñez, F. J. and Roggen, D.: Deep convolutional and lstm
recurrent neural networks for multimodal wearable activity
recognition, Sensors, 16, 115–140
https://doi.org/10.3390/s16010115,
2016.
Parvin, H., Minaei, B., Beigi, A., and Helmi, H.: Classification ensemble
by genetic algorithms, in: International Conference on Adaptive and Natural
Computing Algorithms, 391–399, 14–16 April 2011.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine learning in Python,
J. Mach. Learn. Res., 12, 2825–2830, https://doi.org/10.48550/arXiv.1201.0490, 2011.
Prowse, T. D. and Bonsal, B. R.: Historical trends in river-ice break-up:
a review, Hydrol. Res., 35, 281–293, 2004.
Prowse, T. D., Bonsal, B. R., Duguay, C. R., and Lacroix, M. P.: River-ice
break-up/freeze-up: a review of climatic drivers, historical trends and
future predictions, Ann. Glaciol., 46, 443–451, 2007.
Reback, J., McKinney, W., Den Van Bossche, J., Augspurger, T., Cloud, P.,
Klein, A., Roeschke, M., Hawkins, S., Tratner, J., She, C., Ayd, W., Petersen, T., Garcia, M., Schendel, J., Hayden, A., Mehyar, M., and Seabold, S.: pandas-dev/pandas: Pandas 1.0.3., Zenodo [code], https://doi.org/10.5281/zenodo.3715232,
2020.
Rodríguez, J. J. and Alonso, C. J.: Support vector machines of
interval-based features for time series classification, in: International
Conference on Innovative Techniques and Applications of Artificial
Intelligence, Cambridge, United Kingdom, 244–257, https://doi.org/10.1007/1-84628-102-4_18, 13–15 December 2004.
Sainath, T. N., Vinyals, O., Senior, A., and Sak, H.: Convolutional, long
short-term memory, fully connected deep neural networks, in: International
Conference on Acoustics, Speech and Signal Processing, Brisbane, Australia, 4580–4584, https://doi.org/10.1109/ICASSP.2015.7178838, 19–24 April 2015.
Samek, W. and Müller, K. R.: Towards explainable artificial
intelligence, Explainable AI: Interpreting, Explaining and Visualizing Deep
Learning, 5–22, https://doi.org/10.1109/ICASSP.2015.7178838, 2019.
Samek, W., Wiegand, T., and Müller, K. R.: Explainable artificial
intelligence: Understanding, visualizing and interpreting deep learning
models, arXiv [preprint], arXiv:1708.08296, 28 August 2017.
Sarafanov, M., Borisova, Y., Maslyaev, M., Revin, I., Maximov, G., and
Nikitin, N. O.: Short-Term River Flood Forecasting Using Composite Models
and Automated Machine Learning: The Case Study of Lena River, Water, 13,
3482, https://doi.org/10.3390/w13243482, 2021.
Semenova, N., Sazonov, A., Krylenko, I., and Frolova, N.: Use of
classification algorithms for the ice jams forecasting problem, in: E3S Web of
Conferences, 163, E3S Web Conf., 02008,
https://doi.org/10.1051/e3sconf/202016302008,
2020.
She, X. and Zhang, D.: Text classification based on hybrid CNN-LSTM hybrid
model, in: 11th International Symposium on Computational Intelligence and
Design, 185–189, https://doi.org/10.1109/ISCID.2018.10144, 8–9 December 2018.
Shouyu, C. and Honglan, J.: Fuzzy Optimization Neural Network Approach for
Ice Forecast in the Inner Mongolia Reach of the Yellow River/Approche
d'Optimisation Floue de Réseau de Neurones pour la Prévision de la
Glace Dans le Tronçon de Mongolie Intérieure du Fleuve
Jaune, Hydrolog. Sci. J., 50, 319–330, https://doi.org/10.1623/hysj.50.2.319.61793,
2005.
Sosa, P. M.: Twitter sentiment analysis using combined LSTM-CNN
models, Eprint Arxiv, 1–9, 2017.
Spyder-Documentation: Welcome to Spyder’s Documentation, https://docs.spyder-ide.org/current/index.html, last
access: 20 March 2022.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and
Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from
overfitting, J. Mach. Learn. Res., 15, 1929–1958,
2014.
Thornton, M. M., Shrestha, R., Wei, Y., Thornton, P. E., Kao, S., and Wilson,
B. E.: Daymet: Daily Surface Weather Data on a 1-km Grid for North America,
Version 4, ORNL DAAC, Oak Ridge, Tennessee, USA, Quebec City, QC, Canada, https://doi.org/10.3334/ORNLDAAC/1840, 2020.
Torres, J. F., Hadjout, D., Sebaa, A., Martínez-Álvarez, F., and
Troncoso, A.: Deep Learning for Time Series Forecasting: A Survey, Big
Data, 9, 3–21, 2021.
Turcotte, B. and Morse, B.: River ice breakup forecast and annual risk
distribution in a climate change perspective, in: 18th Workshop on the
Hydraulics of Ice Covered Rivers, CGU HS Committee on River Ice Processes
and the Environment, Quebec City, QC, Canada, 18–20 August 2015.
Umer, M., Imtiaz, Z., Ullah, S., Mehmood, A., Choi, G. S., and On, B. W.:
Fake news stance detection using deep learning architecture (cnn-lstm), IEEE
Access, 8, 156695–156706, 2020.
Understanding LSTM Networks:
https://colah.github.io/posts/2015-08-Understanding-LSTMs/, last access: 20 October 2021.
Wang, J., Yu, L. C., Lai, K. R., and Zhang, X.: Dimensional sentiment
analysis using a regional CNN-LSTM model, in: Proceedings of the 54th Annual
Meeting of the Association for Computational Linguistics, 225–230, Berlin, Germany, https://doi.org/10.18653/v1/P16-2037, 7–12 August 2016.
Wang, J., Yu, L. C., Lai, K. R., and Zhang, X.: Tree-structured regional
CNN-LSTM model for dimensional sentiment analysis, IEEE/ACM Transactions on
Audio, Speech, and Language Processing, 28, 581–591, 2019.
White, K. D.: Review of prediction methods for breakup ice jams, Can.
J. Civil Eng., 30, 89-100, 2003.
White, K. D. and Daly, S. F.: Predicting ice jams with discriminant
function analysis, in: 21st International Conference on Offshore Mechanics and
Arctic Engineering, Oslo, Norway, 683–690, https://doi.org/10.1115/OMAE2002-28563, 23–28 June 2002.
Wojtas, M. and Chen, K.: Feature importance ranking for deep
learning, arXiv [preprint], arXiv:2010.08973, 18 October 2020.
Wong, S. C., Gatt, A., Stamatescu, V., and McDonnell, M. D.: Understanding
data augmentation for classification: when to warp?, in: 2016 international
conference on digital image computing: techniques and applications
(DICTA), Gold Coast, Australia,, IEEE, 1–6, https://doi.org/10.48550/arXiv.1609.08764, 30 November–2 December 2016.
Wu, J., Yao, L., and Liu, B.: An overview on feature-based classification
algorithms for multivariate time series, in: 3rd International Conference on
Cloud Computing and Big Data Analysis, Chengdu, China, 32–38, https://doi.org/10.1109/ICCCBDA.2018.8386483, 20–22 April 2018.
Wu, Z., Wang, X., Jiang, Y. G., Ye, H., and Xue, X.: Modeling
spatial-temporal clues in a hybrid deep learning framework for video
classification, in: 23rd ACM International Conference on Multimedia, Brisbane Australia, 461–470, https://doi.org/10.1145/2733373.2806222, 26–30 October
2015.
Wunsch, A., Liesch, T., and Broda, S.: Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX), Hydrol. Earth Syst. Sci., 25, 1671–1687, https://doi.org/10.5194/hess-25-1671-2021, 2021.
Xing, Z., Pei, J., and Keogh, E.: A brief survey on sequence
classification, ACM Sigkdd Explorations Newsletter, 12, 40–48, 2010.
Xingjian, S. H. I., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., and Woo,
W. C.: Convolutional LSTM network: A machine learning approach for
precipitation nowcasting, Adv. Neural In., 28,
802–810, https://doi.org/10.48550/arXiv.1506.04214, 2015.
Yan, J., Mu, L., Wang, L., Ranjan, R., and Zomaya, A. Y.: Temporal
convolutional networks for the advance prediction of ENSO, Sci.
Rep., 10, 1–15, https://doi.org/10.1038/s41598-020-65070-5, 2020.
Yang, J., Nguyen, M. N., San, P. P., Li, X. L., and Krishnaswamy, S.: Deep
convolutional neural networks on multichannel time series for human activity
recognition, in: Twenty-fourth International Joint Conference on Artificial
Intelligence, Buenos Aires, Argentina, 3995–4001, 25–31 July 2015.
Yi, S., Ju, J., Yoon, M. K., and Choi, J.: Grouped convolutional neural
networks for multivariate time series, arXiv [preprint], arXiv:1703.09938, 29 March
2017.
Zaremba, W., Sutskever, I., and Vinyals, O.: Recurrent neural network
regularization, arXiv [preprint], arXiv:1409.2329, 8 September 2014.
Zhang, D., Lin, J., Peng, Q., Wang, D., Yang, T., Sorooshian, S., Liu, X., and
Zhuang, J.: Modeling and simulating of reservoir operation using the
artificial neural network, support vector regression, deep learning
algorithm, J. Hydrol., 565, 720–736, 2018.
Zhang, Y., Tiňo, P., Leonardis, A., and Tang, K.: A survey on neural
network interpretability, IEEE Transactions on Emerging Topics in
Computational Intelligence, 5, 726–742,
https://doi.org/10.48550/arXiv.2012.14261, 2021.
Zhao, L., Hicks, F. E., and Fayek, A. R.: Applicability of multilayer
feed-forward neural networks to model the onset of river breakup, Cold
Reg. Sci. Technol., 70, 32–42, 2012.
Zheng, Y., Liu, Q., Chen, E., Ge, Y., and Zhao, J. L.: Time series
classification using multi-channels deep convolutional neural networks, in:
International Conference on Web-Age Information Management, 298–310, https://doi.org/10.1007/978-3-319-08010-9_33, 8–9 August 2014.
Zheng, Y., Liu, Q., Chen, E., Ge, Y., and Zhao, J. L.: Exploiting
multi-channels deep convolutional neural networks for multivariate time
series classification, Front. Comput. Sci.-Chi., 10, 96–112, 2016.
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...
