Dynamic prediction of gas concentration based on time series
Existing gas concentration prediction methods could only achieve static gas concentration prediction, could not update with accumulation of gas data, as a result, the prediction results were not timeliness. In view of the above problem, a dynamic prediction method of gas concentration based on time...
| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Industry and Mine Automation
2018-09-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2018040051 |
| _version_ | 1797868975039709184 |
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| author | GUO Siwen TAO Yufan LI Chao |
| author_facet | GUO Siwen TAO Yufan LI Chao |
| author_sort | GUO Siwen |
| collection | DOAJ |
| description | Existing gas concentration prediction methods could only achieve static gas concentration prediction, could not update with accumulation of gas data, as a result, the prediction results were not timeliness. In view of the above problem, a dynamic prediction method of gas concentration based on time series was proposed. Firstly, the method uses multi-resolution characteristic of wavelet decomposition technique to decompose the gas concentration time series to different scales to make the time series smooth. Then it adopts auto regressive and moving average(ARMA) model constructed by real-time and dynamic data to predict mine gas concentration in the future time by use of gas concentration change trend in the past time, so as to obtain time series prediction results. Finally, in order to improve the accuracy of the gas concentration prediction, the prediction results of the ARMA model and mine environment parameters are input into the trained BP neural network model, and the prediction results are corrected by the BP neural network model, so as to obtain final gas concentration prediction value. The test results show that the method can accurately predict the mine gas concentration, and the average relative error of gas concentration prediction is reduced from 8% to 5%. |
| first_indexed | 2024-04-10T00:04:13Z |
| format | Article |
| id | doaj.art-33126308dfdd4cab80ab4c66d9986a04 |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-04-10T00:04:13Z |
| publishDate | 2018-09-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-33126308dfdd4cab80ab4c66d9986a042023-03-17T01:19:10ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2018-09-01449202510.13272/j.issn.1671-251x.2018040051Dynamic prediction of gas concentration based on time seriesGUO SiwenTAO YufanLI ChaoExisting gas concentration prediction methods could only achieve static gas concentration prediction, could not update with accumulation of gas data, as a result, the prediction results were not timeliness. In view of the above problem, a dynamic prediction method of gas concentration based on time series was proposed. Firstly, the method uses multi-resolution characteristic of wavelet decomposition technique to decompose the gas concentration time series to different scales to make the time series smooth. Then it adopts auto regressive and moving average(ARMA) model constructed by real-time and dynamic data to predict mine gas concentration in the future time by use of gas concentration change trend in the past time, so as to obtain time series prediction results. Finally, in order to improve the accuracy of the gas concentration prediction, the prediction results of the ARMA model and mine environment parameters are input into the trained BP neural network model, and the prediction results are corrected by the BP neural network model, so as to obtain final gas concentration prediction value. The test results show that the method can accurately predict the mine gas concentration, and the average relative error of gas concentration prediction is reduced from 8% to 5%.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2018040051mine gas concentration predictiondynamic prediction of gas concentrationtime serieswavelet decompositionauto regressive and moving average modelbp neural network |
| spellingShingle | GUO Siwen TAO Yufan LI Chao Dynamic prediction of gas concentration based on time series Gong-kuang zidonghua mine gas concentration prediction dynamic prediction of gas concentration time series wavelet decomposition auto regressive and moving average model bp neural network |
| title | Dynamic prediction of gas concentration based on time series |
| title_full | Dynamic prediction of gas concentration based on time series |
| title_fullStr | Dynamic prediction of gas concentration based on time series |
| title_full_unstemmed | Dynamic prediction of gas concentration based on time series |
| title_short | Dynamic prediction of gas concentration based on time series |
| title_sort | dynamic prediction of gas concentration based on time series |
| topic | mine gas concentration prediction dynamic prediction of gas concentration time series wavelet decomposition auto regressive and moving average model bp neural network |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2018040051 |
| work_keys_str_mv | AT guosiwen dynamicpredictionofgasconcentrationbasedontimeseries AT taoyufan dynamicpredictionofgasconcentrationbasedontimeseries AT lichao dynamicpredictionofgasconcentrationbasedontimeseries |