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

Abstract

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.

Lanthanide molecular nanomagnets as probabilistic bits

“…To quantify the correlation, we consider the four states (0, 0), (0, 1), (1, 0), (1,1) where the first and second index correspond to the state of p-bit i and j respectively, and N(i, j) as the number of such states in a continuous run of the simulation. We define the correlation c between p-bit i and p-bit j as: What one observes in Figure 4(b) is a base instantaneous correlation, that is enhanced by allowing a certain delay time between the stimulus and the response, to accommodate the intrinsic dynamics, and which is then gradually lost for longer delay times, until only noise is observed: c ≃ 1, i.e.…”

Lanthanide molecular nanomagnets as probabilistic bits

“…To quantify the correlation, we consider the four states (0, 0), (0, 1), (1, 0), (1,1) where the first and second index correspond to the state of p-bit I and II respectively, and N(i, j) as the number of such states in a continuous run of the simulation. We define the correlation c between p-bit I and p-bit II as: We reveal a possibility in Figure 4(c).…”

“…The rising of Artificial Intelligence (AI) has been instrumental for pattern recognition 1 , reasoning under uncertainty 2 , control methods 3 , analyzing and classifying big data. 4,5 Nevertheless, there is a need for scalable and energy-efficient hardware constructed following the same scheme: further progress of AI algorithms depends on the efficiency of its hardware.…”

“…where I i,[−1,1] is its input signal, normalized between -1 and 1, and 1] is a random number with uniform distribution between -1 and +1, meaning that the output is stochastic but biased by the input. It is only unbiased when I i = 0 and only deterministic when I i = 1 or I i = −1.…”

Climate and environmental data contribute to the prediction of grain commodity prices using deep learning

“…The better performance of the CNN over the LSTM at 5 weeks may be attributed to the ability of the CNN to partially include the temporal dependence of the dataset (Madaeni et al, 2022). However, models in that the CNN is better able to capture correlations between variables, while the LSTM deals with the temporal dynamics of the input variables (Madaeni et al, 2022;Ordóñez & Roggen, 2016).…”