Convolutional neural network and long short-term memory models for ice-jam predictions

• Published in: The cryosphere Vol. 16; no. 4; pp. 1447 - 1468
• Main Authors: Madaeni, Fatemehalsadat, Chokmani, Karem, Lhissou, Rachid, Homayouni , Saeid, Gauthier, Yves, Tolszczuk-Leclerc, Simon
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 22-04-2022; Copernicus Publications
• Subjects: Accuracy; Analysis; Artificial neural networks; Brittleness; Classification; Cold regions; Data mining; Datasets; Decision trees; Deep learning; Flood predictions; Flooding; Floods; Hydrometeorology; Ice; Ice formation; Ice jams; Learning algorithms; Long short-term memory; Machine learning; Medicine; Meteorological conditions; Modelling; Multilayer perceptrons; Neural networks; Predictions; Property damage; Response time; Rivers; Snow accumulation; Snow depth; Support vector machines; Temperature perception; Testing; Time series; Variables; Water levels; Weather forecasting
• ISSN: 1994-0424; 1994-0416; 1994-0424; 1994-0416
• DOI: 10.5194/tc-16-1447-2022

In cold regions, ice jams frequently result in severe
flooding due to a rapid rise in water levels upstream of the jam. Sudden
floods resulting from ice jams threaten human safety and cause damage to
properties and infrastructure. Hence, ice-jam prediction tools can give an
early warning to increase response time and minimize the possible damages.
However, ice-jam prediction has always been a challenge as there is no
analytical method available for this purpose. Nonetheless, ice jams form
when some hydro-meteorological conditions happen, a few hours to a few days
before the event. Ice-jam prediction can be addressed as a binary
multivariate time-series classification. Deep learning techniques have been
widely used for time-series classification in many fields such as finance,
engineering, weather forecasting, and medicine. In this research, we
successfully applied convolutional neural networks (CNN), long short-term
memory (LSTM), and combined convolutional–long short-term memory (CNN-LSTM)
networks to predict the formation of ice jams in 150 rivers in the province
of Quebec (Canada). We also employed machine learning methods including
support vector machine (SVM), k-nearest neighbors classifier (KNN), decision
tree, and multilayer perceptron (MLP) for this purpose. The hydro-meteorological variables (e.g., temperature, precipitation, and snow
depth) along with the corresponding jam or no-jam events are used as model
inputs. Ten percent of the data were excluded from the model and set aside
for testing, and 100 reshuffling and splitting iterations were applied to
80 % of the remaining data for training and 20 % for validation. The
developed deep learning models achieved improvements in performance in
comparison to the developed machine learning models. The results show that
the CNN-LSTM model yields the best results in the validation and testing
with F1 scores of 0.82 and 0.92, respectively. This demonstrates that CNN
and LSTM models are complementary, and a combination of both further
improves classification.