Coal and gas outburst prediction model based on extension theory and its application

2021 ◽  
Author(s):  
Wei Wang ◽  
Hanpeng Wang ◽  
Bing Zhang ◽  
Su Wang ◽  
Wenbin Xing
Keyword(s):  
Prediction Model ◽  
Coal And Gas Outburst ◽  
Extension Theory ◽  
Gas Outburst ◽  
Model Based

Outburst prediction and influencing factors analysis based on Boruta-Apriori and BO-SVM algorithms

2021 ◽  
pp. 1-18
Author(s):  
Zhang Zixian ◽  
Liu Xuning ◽  
Li Zhixiang ◽  
Hu Hongqiang
Keyword(s):  
Feature Selection ◽  
Prediction Model ◽  
Influencing Factors ◽  
Association Rules ◽  
Coal And Gas Outburst ◽  
Feature Subset ◽  
Gas Outburst ◽  
Proposed Model ◽  
Optimal Feature Subset ◽  
Feature Dimension

The influencing factors of coal and gas outburst are complex, now the accuracy and efficiency of outburst prediction and are not high, in order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outbursts based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outbursts prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved, However, the feature dimension decreased significantly; The results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model, and has high stability and robustness.

Intensity of Coal and Gas Outburst Prediction Model Based on the Improved BP Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 1190-1193
Author(s):  
Long Kong
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Forecast Accuracy ◽  
Coal And Gas Outburst ◽  
Mine Safety ◽  
Simulation Test ◽  
Gas Outburst ◽  
Major Disaster

Coal and gas outburst has become one of the major disaster hazard of coal mine safety, Staff on gas outburst disaster prevention is now important research project. The gas outburst prediction work, different degrees of factors has some impact on forecast accuracy, such as logical reasoning efficiency is low. This paper, by using the BP neural network combined with gas outburst samples a prediction model is established, According to the data of a certain coal mine as a sample, Using MATLAB software to simulation test, have been predicted and actual values fitting degree is higher, Can reflect the realities of the coal and gas outburst.

scholarly journals Real-Time Prediction Model of Coal and Gas Outburst

2020 ◽  
Vol 2020 ◽  
pp. 1-5
Author(s):  
Ru Yandong ◽  
Lv Xingfeng ◽  
Guo Jikun ◽  
Zhang Hongquan ◽  
Chen Lijuan
Keyword(s):  
Missing Data ◽  
Prediction Model ◽  
Real Time ◽  
Prediction Performance ◽  
Coal And Gas Outburst ◽  
Mine Safety ◽  
Gas Outburst ◽  
Coal Mine Safety ◽  
Data Value ◽  
First Time

Coal and gas outburst has been one of the main threats to coal mine safety. Accurate coal and gas outburst prediction is the key to avoid accidents. The data is actual and complete by default in the existing prediction model. However, in fact, data missing and abnormal data value often occur, which results in poor prediction performance. Therefore, this paper proposes to use the correlation coefficient to complete the missing data filling in real time for the first time. The abnormal data identification is completed based on the Pauta criterion. Random forest model is used to realize the prediction model. The prediction performance of sensitivity 100%, accuracy 97.5%, and specificity 84.6% were obtained. Experiments show that the model can complete the prediction of coal and gas outburst in real time under the condition of missing data and abnormal data value, which can be used as a new prediction model of coal and gas outburst.

scholarly journals A Continuous Dynamic Prediction Model of Gas Pressure Based on Gas Emission at Excavation Face and its Engineering Application

2016 ◽  
Author(s):  
Chen Liang ◽  
Wang Enyuan
Keyword(s):  
Prediction Model ◽  
Prevention And Control ◽  
Engineering Application ◽  
Dynamic Effect ◽  
Gas Pressure ◽  
Coal And Gas Outburst ◽  
Gas Emission ◽  
Dynamic Prediction ◽  
Gas Outburst ◽  
And Control

Abstract. Gas pressure is one of the necessary conditions for the occurrence of coal and gas outburst. Realization of continuous and dynamic gas pressure forecasting is of significance for prevention and control of coal and gas outburst. In this work, we established a gas pressure prediction model based on the source of gas emission with considering fluid-solid coupling process. The verified results showed that the predicted gas pressure was roughly consistent with the actual situation, indicating that the prediction model is correct. And it could meet the need of engineering projects. Coal and gas outburst dynamic phenomenon is successfully predicted in engineering application with the model. Overall, prediction coal and gas outburst with the gas pressure model can achieve the continuous and dynamic effect. It can overcome both the static and sampling shortcomings of traditional methods, and solve the difficulty of coal and gas outburst prediction at the excavation face. With its broad applicability and potential prospect, we believe the model is of great importance for improving prevention and control of gas disasters.

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