A Gas Outburst Prediction Model Based on Data Mining and Information Fusion

Abstract

The gas outburst in coalmines is influenced by multiple factors. These influencing factors are highly uncertain and have complex nonlinear relationships. Considering these features, this paper puts forward a gas outburst prediction model based on data mining and information fusion. On the feature level, the backpropagation neural network (BPNN) was selected to set up a gas outburst identification model, thanks to its strong self-learning ability, and then optimized by the improved particle swarm optimization (IPSO); then, the outputs of the optimized BPNN were taken as the identification results, and used to establish a feature database. On the decision level, the Dempster-Shafter (D-S) theory of evidence was introduced to fuse the identification results in the time domain and the spatial domain, and make decisions on the gas state of the coalmine based on the fused data. Finally, the proposed model was applied the predict the gas outburst in a coalmining area of a coalmine in Shanxi Province, China, using the data collected from the workface, intake airway, return airway and transport roadway. Our model fuses the data on two layers, namely, the time domain and the spatial domain, and reduces the prediction uncertainty to zero. The results show that our model can optimize the prediction parameters, enhance the accuracy of gas monitoring information, and improve the correctness of decisions concerning gas outburst in the coalmine.

Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

“…Based on the improved entropy weight grey correlation analysis algorithm, standardize the initial data by employing equations (2) and (3), and thus calculate the entropy weight of each influencing factor by employing equations (4) and (6). e results are shown in Table 2.…”

“…Over the past years, gas outburst prediction research has achieved fruitful results [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Numerous approaches are currently used to predict the gas outburst.…”

“…With the rapid development of data mining technology and machine learning, some methods such as data mining algorithms [5,6], neural networks [7], D-S evidence theory [8], rough set theory [9], machine learning algorithms [10][11][12], support vector machines (SVM) [13][14][15], and wavelet transform [16,17] have been gradually adopted in the field of gas outburst prediction. In [6], BP neural network and D-S theory were used to establish the gas outburst prediction model: the BP neural network was optimized by PSO in the feature layer and D-S theory was used to make the fusion decision of time domain and space domain. A PCA-BP neural network prediction model was established; three principal components were extracted through the PCA method [7].…”

A novel combined intelligent algorithm prediction model for the risk of the coal and gas outburst

“…Many studies have been conducted based on prediction algorithms and models in coal mines 20 – 25 . With the concept of accurate and intelligent mining in coal mines put forward, researchers pay more and more attention to machine learning to predict coal and gas outbursts 26 – 29 . Such as, Particle Swarm Optimization (PSO) is used to optimize the prediction model of the back propagation (BP) algorithm 26 , random forest model is used to predict coal and gas outbursts 27 , and genetic algorithm is used to optimize the support vector machine model 28 .…”

“…With the concept of accurate and intelligent mining in coal mines put forward, researchers pay more and more attention to machine learning to predict coal and gas outbursts 26 – 29 . Such as, Particle Swarm Optimization (PSO) is used to optimize the prediction model of the back propagation (BP) algorithm 26 , random forest model is used to predict coal and gas outbursts 27 , and genetic algorithm is used to optimize the support vector machine model 28 . Given the lack of sample data, Zheng et al used the data mining Multiple Imputation (MI) method to fill the missing data, and used the support vector machine (SVM) to predict coal and gas outburst 29 .…”