Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review

Madaeni, Fatemehalsadat; Lhissou, Rachid; Chokmani, Karem; Raymond, Sébastien; Gauthier, Yves

doi:10.1016/j.coldregions.2020.103032

Cited by 21 publications

(15 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Turcotte and Morse (2015, August) explain that Accumulated Thawing Degree Day (ATDD), an indicator of warming periods, partially covers the effect of shortwave radiation. In the previous studies of ice-jam and breakup predictions, discharge and changes in discharge, water level and changes in water level, AFDD, ATDD, precipitation, solar radiation, heat budget, and snowmelt or snowpack are the most readily used variables (Madaeni et al, 2020).…”

Section: Input Data and Study Areamentioning

confidence: 99%

Convolutional Neural Network and Long Short-Term Memory Models for Ice-Jam Prediction

Madaeni¹,

Chokmani²,

Lhissou³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. In cold regions, ice-jam events result in severe flooding due to a rapid rise in water levels upstream of the jam. These floods threaten human safety and damage properties and infrastructures as the floods resulting from ice-jams are sudden. Hence, the ice-jam prediction tools can give an early warning to increase response time and minimize the possible corresponding damages. However, the ice-jam prediction has always been a challenging problem 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. The ice-jam prediction problem can be considered as a binary multivariate time-series classification. Deep learning techniques have been successfully applied for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied CNN, LSTM, and combined CN-LSTM networks for ice-jam prediction for all the rivers in Quebec. The results show that the CN-LSTM model yields the best results in the validation and generalization with F1 scores of 0.82 and 0.91, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of them further improves classification.

show abstract

Section: Input Data and Study Areamentioning

confidence: 99%

Convolutional Neural Network and Long Short-Term Memory Models for Ice-Jam Prediction

Madaeni¹,

Chokmani²,

Lhissou³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…En (Liu et al, 2018) se emplean técnicas micro sísmicas para prevenir el estallido de rocas en las Centrales hidroeléctricas. Así mismo, autores como Madaeni et al, (2020), muestran como estas técnicas pueden ser empleadas en diversos procesos industriales. Otros autores como Xue et al, (2020), emplean las técnicas inteligentes en la predicción del riesgo de estallido de rocas subterráneas.…”

Section: Otros Autores Comounclassified

Predicción de parada de máquinas generadoras en una central hidroeléctrica por medio de minería de datos

2020

View full text Add to dashboard Cite

show abstract

“…The subsequent simplifications necessary to model application decrease model accuracy (Shouyu and F. Madaeni et al: Convolutional neural network and long short-term memory models Honglan, 2005). A detailed overview of the previous models for ice-jam prediction based on hydro-meteorological data is presented in Madaeni et al (2020).…”

Section: Introductionmentioning

confidence: 99%

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

Madaeni¹,

Chokmani²,

Lhissou³

et al. 2022

The Cryosphere

Self Cite

View full text Add to dashboard Cite

Abstract. 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.

show abstract

Ice jam formation, breakup and prediction methods based on hydroclimatic data using artificial intelligence: A review

Cited by 21 publications

References 52 publications

Convolutional Neural Network and Long Short-Term Memory Models for Ice-Jam Prediction

Convolutional Neural Network and Long Short-Term Memory Models for Ice-Jam Prediction

Predicción de parada de máquinas generadoras en una central hidroeléctrica por medio de minería de datos

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

Contact Info

Product

Resources

About