Uncertainty Prediction of Mining Safety Production Situation

Abstract In order to explore the occurrence and development law of mining safety production accidents, analyze its future change trends, and aim at the ambiguity, non-stationarity, and randomness of mining safety production accidents, an uncertainty prediction model for mining safety production situation is proposed. Firstly, the time series effect evaluation function is introduced to determine the optimal time granularity, which is used as the window width of fuzzy information granulation (FIG), and the time series of mining safety production situation is mapped to Low, R and Up three granular parameter sequences, according to the triangular fuzzy number; Then, the mean value of the intrinsic mode function (IMF) is maintained in the normal dynamic filtering range. After the ensemble empirical mode decomposition (EEMD), the three non-stationary granulation parameter sequences of Low, R and Up are decomposed into the intrinsic mode function components representing the detail information and the trend components representing the overall change, and then the sub-sequences are reconstructed according to the sample entropy to highlight the correlation among the sub-sequences; Finally, the cloud model language rules of mining safety production situation prediction are created. Through time series discretization, cloud transformation, concept jump, time series set division, association rule mining and uncertain reasoning, the reconstructed component sequence is modeled and predicted by uncertainty information extraction. The accuracy of the uncertainty prediction model was verified by 21 sets of test samples. The average relative errors of Low, R and Up sequences were 9.472 %, 16.671 % and 3.625 %, respectively. The research shows that the uncertainty prediction model of mining safety production situation overcomes the fuzziness, non-stationarity and uncertainty of safety production accidents, and provides theoretical reference and practical guidance for mining safety management and decision-making.

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EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

Complexity ◽

10.1155/2020/1341047 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Menglong Wu ◽

Yicheng Ye ◽

Nanyan Hu ◽

Qihu Wang ◽

Huimin Jiang ◽

...

Keyword(s):

Time Series ◽

Prediction Accuracy ◽

Signal Sequence ◽

Low Frequency ◽

Arma Model ◽

Short Term ◽

Svm Model ◽

Safety Production ◽

Mining Safety ◽

Production Situation

In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used to predict high-frequency signal sequence, while the ARMA model was used to predict low-frequency signal sequence. Finally, aiming to predict the mining safety production situation, the EMD-GM-ARMA model was constructed via superimposing the prediction results of each subsequence, thereby compared to the ARIMA model, wavelet neural network model, and PSO-SVM model. The results demonstrated that the EMD-GM-ARMA model and the PSO-SVM model hold the highest prediction accuracy in the short-term prediction, and the wavelet neural network has the lowest prediction accuracy. The PSO-SVM model’s prediction accuracy decreases in medium- and long-term predictions while the EMD-GM-ARMA model still can maintain high prediction accuracy. Moreover, the relative error fluctuations of the EMD-GM-ARMA model are relatively stable in both short-term and medium-term predictions. This shows that the EMD-GM-ARMA model can provide high-precision predictions with high stability, proving the model to be feasible and effective in predicting the mining safety production situation.

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Noise Reduction for Nonlinear Nonstationary Time Series Data using Averaging Intrinsic Mode Function

Algorithms ◽

10.3390/a6030407 ◽

2013 ◽

Vol 6 (3) ◽

pp. 407-429 ◽

Cited By ~ 7

Author(s):

Bhusana Premanode ◽

Jumlong Vongprasert ◽

Christofer Toumazou

Keyword(s):

Time Series ◽

Noise Reduction ◽

Time Series Data ◽

Intrinsic Mode Function ◽

Nonstationary Time Series ◽

Series Data ◽

Mode Function

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Construct Validity

10.1093/oso/9780190661557.003.0008 ◽

2017 ◽

Author(s):

Richard McCleary ◽

David McDowall ◽

Bradley J. Bartos

Keyword(s):

Time Series ◽

Construct Validity ◽

Hypothesis Test ◽

Secondary Data ◽

Time Frame ◽

Primary Data ◽

Optimal Time ◽

Intervention Model ◽

Right Hand ◽

The Right

Chapter 8 focuses on threats to construct validity arising from the left-hand side time series and the right-hand side intervention model. Construct validity is limited to questions of whether an observed effect can be generalized to alternative cause and effect measures. The “talking out” self-injurious behavior time series, shown in Chapter 5, are examples of primary data. Researchers often have no choice but to use secondary data that were collected by third parties for purposes unrelated to any hypothesis test. Even in those less-than-ideal instances, however, an optimal time series can be constructed by limiting the time frame and otherwise paying attention to regime changes. Threats to construct validity that arise from the right-hand side intervention model, such as fuzzy or unclear onset and responses, are controlled by paying close attention to the underlying theory. Even a minimal theory should specify the onset and duration of an impact.

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