Measure Dry Beach Length of Tailings Pond Using Deep Learning Algorithm

Yang, Jun; Sun, Yuqing; Li, Qing; Qian, Zhiyu

doi:10.1145/3366194.3366284

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Recently, with the advantages of handling almost any nonlinear and linear problems, regardless of low and high dimensions, neural network and machine learning methods have been effectively composed in real-time risk analysis and evaluation [22]- [29]. However, the role of real-time monitoring cannot be equated with early warning and forecasting.…”

Section: Related Workmentioning

confidence: 99%

Saturation Line Forecasting via a Channel and Temporal Attention-Based Network

Wang

Li²,

Wang

et al. 2022

IEEE Access

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Tailings ponds are places for storing industrial waste. The saturation line is the crucial factor in quantifying the safety of tailings ponds. Existing saturation line time-series prediction methods are mainly based on statistical models or shallow machine learning models. Although these models aim to capture the time dependence of the sequence data, the channel and temporal are even unavailable in principle. To mitigate this problem, in this paper, we present a two-stage forecasting method, which embeds the channel and temporal attention into a hybrid CNN-LSTM model to predict the saturation line. The channel and temporal attention are utilized to capture subtle high-dimensional time-series dependence. In the first stage, the discrete wavelet transform (DWT) is applied to capture the refined sequence information. In the second stage, the CNN-LSTM model is utilized to learn the basic spatial and temporal features in the time series. Furthermore, the channel and temporal attention model are embedded into the CNN-LSTM model to enhance the feature-extracting ability in the channel and temporal dimensions. Consequently, our proposed model is shown to outperform classic models on multiple real-world datasets in terms of RMSE, MAPE, R 2 and MAE, respectively.

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Section: Related Workmentioning

confidence: 99%

Saturation Line Forecasting via a Channel and Temporal Attention-Based Network

Wang

Li²,

Wang

et al. 2022

IEEE Access

Self Cite

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“…As a result, the stability of the tailings pond cannot be directly observed. Recently, with the advantages of handling almost any non-linear and linear problems whatever low-and high-dimensions, neural network and machine learning methods have been effectively composed in real-time risk analysis and evaluation [4,9,12,19,[43][44][45][46]. However, the role of real-time monitoring cannot be equated with early warning and forecasting.…”

Section: Introductionmentioning

confidence: 99%

A CNN-LSTM Model for Tailings Dam Risk Prediction

Yang

et al. 2020

IEEE Access

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Tailings ponds are places for storing industrial waste. Once the tailings pond collapses, the villages nearby will be destroyed and the harmful chemicals will cause serious environmental pollution. There is an urgent need for a reliable forecast model, which could investigate the variation trend of stability coefficient of tailing dam and issue early warnings. In order to fill the gap, this work presents an hybrid network -Wavelet-based Long-Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), namely Wavelet-CNN-LSTM netwrok for predicting the tailings pond risk. Firstly, we construct the especial nonlinear data processing method to impute the missing value with the numerical inversion (NI) method, which combines correlation analysis, sensitivity analysis, and Random Forest (RF) algorithms. Secondly, a new forecasting model was proposed to monitor the saturation line, which is the lifeline of the tailings pond and can directly reflect the stability of the tailings pond. After using the discrete wavelet transform (DWT) to decompose the original saturation line data into 4-layer wavelets and de-noise the data, the CNN was used to identify and learn the spatial structures in the time series, followed by LSTM cells for detecting the long-short-term dependence. Finally, different experiments were conducted to evaluate the effectiveness of our model by comparing it with other state-of-the-art algorithms. The results show that Wavelet-CNN-LSTM achieves the best score both in mean absolute percentage error (MAPE), root-mean-square error (RMSE) and R 2 .

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Effective Risk Prediction of Tailings Ponds Using Machine Learning

Yang

Sun

et al. 2020

2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)

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Measure Dry Beach Length of Tailings Pond Using Deep Learning Algorithm

Cited by 5 publications

References 2 publications

Saturation Line Forecasting via a Channel and Temporal Attention-Based Network

Saturation Line Forecasting via a Channel and Temporal Attention-Based Network

A CNN-LSTM Model for Tailings Dam Risk Prediction

Effective Risk Prediction of Tailings Ponds Using Machine Learning

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