Effective Risk Prediction of Tailings Ponds Using Machine Learning

Yang, Jun; Sun, Yuqing; Li, Qing; Sun, Yixuan

doi:10.1109/aemcse50948.2020.00057

Cited by 4 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At present, a large number of researchers are devoted on tailings pond monitoring [2,14,26,41,42]. The researchers are mainly focusing on the stability status by monitoring data from sensors and make early-warnings in time by mathematical modeling method, image recognition method and data analysis.…”

Section: Introductionmentioning

confidence: 99%

A CNN-LSTM Model for Tailings Dam Risk Prediction

Yang

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

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 .

show abstract

Section: Introductionmentioning

confidence: 99%

A CNN-LSTM Model for Tailings Dam Risk Prediction

Yang

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

Proactive control model for safety prediction in tailing dam management: Applying graph depth learning optimization

Ruan

Han

et al. 2023

Process Safety and Environmental Protection

View full text Add to dashboard Cite

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

Wang

Li²,

Wang

et al. 2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Effective Risk Prediction of Tailings Ponds Using Machine Learning

Cited by 4 publications

References 5 publications

A CNN-LSTM Model for Tailings Dam Risk Prediction

A CNN-LSTM Model for Tailings Dam Risk Prediction

Proactive control model for safety prediction in tailing dam management: Applying graph depth learning optimization

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

Contact Info

Product

Resources

About