Research on time series characteristics of the gas drainage evaluation index based on lasso regression

AbstractThe evaluation of the coal mine gas drainage effect is affected by many factors, such as flow rate, wind speed, drainage negative pressure, concentration, and temperature. This paper starts from actual coal mine production monitoring data and based on the lasso regression algorithm, features selection of multiple parameters of the preprocessed gas concentration time series to construct gas concentration feature selection based on the algorithm. The three-time smoothing index method is used to fill in the missing values. Aiming at the problem of different dimensions in the gas concentration time series, the MinMaxScaler method is used to normalize the data. The lasso regression algorithm is used to perform feature selection on the multivariable gas concentration time series, and the gas concentration time series selected by the lasso feature and the gas concentration time series without feature selection are input. The performance of the ANN algorithm for gas concentration prediction is compared and analyzed. The optimal α value and L1 norm are selected based on the grid search method to determine the strong explanatory gas concentration time series feature set of the working face, and an experimental comparison of the gas concentration prediction results before and after the lasso feature selection is performed. We verify the effectiveness of the algorithm.

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Research on Gas Concentration Prediction Models Based on LSTM Multidimensional Time Series

Energies ◽

10.3390/en12010161 ◽

2019 ◽

Vol 12 (1) ◽

pp. 161 ◽

Cited By ~ 12

Author(s):

Tianjun Zhang ◽

Shuang Song ◽

Shugang Li ◽

Li Ma ◽

Shaobo Pan ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Coal Mine ◽

Time Series Prediction ◽

Mean Square ◽

Gas Concentration ◽

Concentration Time ◽

Concentration Time Series ◽

Concentration Prediction

Effective prediction of gas concentrations and reasonable development of corresponding safety measures have important guiding significance for improving coal mine safety management. In order to improve the accuracy of gas concentration prediction and enhance the applicability of the model, this paper proposes a long short-term memory (LSTM) cyclic neural network prediction method based on actual coal mine production monitoring data to select gas concentration time series with larger samples and longer time spans, including model structural design, model training, model prediction, and model optimization to implement the prediction algorithm. By using the minimum objective function as the optimization goal, the Adam optimization algorithm is used to continuously update the weight of the neural network, and the network layer and batch size are tuned to select the optimal one. The number of layers and batch size are used as parameters of the coal mine gas concentration prediction model. Finally, the optimized LSTM prediction model is called to predict the gas concentration in the next time period. The experiment proves the following: The LSTM gas concentration prediction model uses large data volume sample prediction, more accurate than the bidirectional recurrent neural network (BidirectionRNN) model and the gated recurrent unit (GRU) model. The average mean square error of the prediction model can be reduced to 0.003 and the predicted mean square error can be reduced to 0.015, which has higher reliability in gas concentration time series prediction. The prediction error range is 0.0005–0.04, which has better robustness in gas concentration time series prediction. When predicting the trend of gas concentration time series, the gas concentration at the time inflection point can be better predicted and the mean square error at the inflection point can be reduced to 0.014, which has higher applicability in gas concentration time series prediction.

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The Prediction of Multi-Scale Entropy on Gas Concentration Time Series Based on Variational Mode Decomposition

Journal of Nanoelectronics and Optoelectronics ◽

10.1166/jno.2019.2641 ◽

2019 ◽

Vol 14 (9) ◽

pp. 1313-1325

Author(s):

Wei Dai ◽

Chang-Peng Ji ◽

Ying-Jie Wang

Keyword(s):

Time Series ◽

Variational Mode Decomposition ◽

Gas Concentration ◽

Multi Scale ◽

Concentration Time ◽

Mode Decomposition ◽

Concentration Time Series

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Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

Mathematical Problems in Engineering ◽

10.1155/2014/858260 ◽

2014 ◽

Vol 2014 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Wu Xiang ◽

Qian Jian-sheng ◽

Huang Cheng-hua ◽

Zhang Li

Keyword(s):

Time Series ◽

High Frequency ◽

Prediction Models ◽

Forecasting Model ◽

Gas Concentration ◽

Concentration Time ◽

Proposed Model ◽

Concentration Time Series ◽

Predicted Values ◽

Learning Machine

It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction.

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Anomaly Detection for Time Series with Difference Rate Sample Entropy and Generative Adversarial Networks

Complexity ◽

10.1155/2021/5854096 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Keke Gao ◽

Wenbin Feng ◽

Xia Zhao ◽

Chongchong Yu ◽

Weijun Su ◽

...

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Normal Mode ◽

Coal Mine ◽

Spontaneous Combustion ◽

Sample Entropy ◽

Generative Adversarial Networks ◽

Detection Accuracy ◽

Gas Concentration ◽

Adversarial Networks

The spontaneous combustion of residual coals in the mined-out area tends to cause an explosion, which is one kind of severe thermodynamic compound disaster of coal mines and leads to serious losses to people's lives and production safety. The prediction and early warning of coal mine thermodynamic disasters are mainly determined by the changes of the index gas concentration pattern in coal mine mined-out areas collected continuously. The time series anomaly pattern detection method is mainly used to reach the state change of gas concentration pattern. The change of gas concentration follows a certain rule as time changes. A great change in the gas concentration indicates the possibility of coal spontaneous combustion and other disasters. To emphasize the features of collected maker gas and overcome the low anomaly detection accuracy caused by the inadequate learning of the normal mode, this paper adopted a method of anomaly detection for time series with difference rate sample entropy and generative adversarial networks. Because the difference rate entropy feature of abnormal data was much larger than that of normal mode, this paper improved the calculation method of the abnormal score by giving different weights to the detection points to enhance the detection rate. To verify the effectiveness of the proposed method, this paper employed simulation models of the mined-out area and adopted coal samples from Dafosi Coal Mine to carry out experiments. Preliminary testing was performed using monitoring data from a coal mine. The experiment compared the entropy results of different time series with the detection results of generative adversarial networks and automatic encoders and showed that the method proposed in this paper had relatively high detection accuracy.

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Analysis and generation of groundwater concentration time series

Advances in Water Resources ◽

10.1016/j.advwatres.2017.10.039 ◽

2018 ◽

Vol 111 ◽

pp. 20-30 ◽

Cited By ~ 3

Author(s):

Maria Crăciun ◽

Călin Vamoş ◽

Nicolae Suciu

Keyword(s):

Time Series ◽

Concentration Time ◽

Concentration Time Series

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Cluster analysis of WIBS single-particle bioaerosol data

Atmospheric Measurement Techniques ◽

10.5194/amt-6-337-2013 ◽

2013 ◽

Vol 6 (2) ◽

pp. 337-347 ◽

Cited By ~ 52

Author(s):

N. H. Robinson ◽

J. D. Allan ◽

J. A. Huffman ◽

P. H. Kaye ◽

V. E. Foot ◽

...

Keyword(s):

Time Series ◽

Cluster Analysis ◽

Single Particle ◽

Fungal Spores ◽

Polystyrene Latex ◽

Data Sets ◽

Forest Site ◽

Fluorescence Measurements ◽

Concentration Time ◽

Concentration Time Series

Abstract. Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP) measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

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