EMD-GM-ARMA Model for Mining Safety Production Situation Prediction
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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/1341047 |
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| _version_ | 1832568564083589120 |
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| author | Menglong Wu Yicheng Ye Nanyan Hu Qihu Wang Huimin Jiang Wen Li |
| author_facet | Menglong Wu Yicheng Ye Nanyan Hu Qihu Wang Huimin Jiang Wen Li |
| author_sort | Menglong Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-497f854048674fcfbe57c2970c20ef2a |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-497f854048674fcfbe57c2970c20ef2a2025-02-03T00:58:44ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/13410471341047EMD-GM-ARMA Model for Mining Safety Production Situation PredictionMenglong Wu0Yicheng Ye1Nanyan Hu2Qihu Wang3Huimin Jiang4Wen Li5School of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, Hubei 430081, ChinaSchool of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, Hubei 430081, ChinaSchool of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, Hubei 430081, ChinaSchool of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, Hubei 430081, ChinaSchool of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, Hubei 430081, ChinaSchool of Resource and Environmental Engineering, Wuhan University of Science and Technology, Wuhan, Hubei 430081, ChinaIn 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.http://dx.doi.org/10.1155/2020/1341047 |
| spellingShingle | Menglong Wu Yicheng Ye Nanyan Hu Qihu Wang Huimin Jiang Wen Li EMD-GM-ARMA Model for Mining Safety Production Situation Prediction Complexity |
| title | EMD-GM-ARMA Model for Mining Safety Production Situation Prediction |
| title_full | EMD-GM-ARMA Model for Mining Safety Production Situation Prediction |
| title_fullStr | EMD-GM-ARMA Model for Mining Safety Production Situation Prediction |
| title_full_unstemmed | EMD-GM-ARMA Model for Mining Safety Production Situation Prediction |
| title_short | EMD-GM-ARMA Model for Mining Safety Production Situation Prediction |
| title_sort | emd gm arma model for mining safety production situation prediction |
| url | http://dx.doi.org/10.1155/2020/1341047 |
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