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

Abstract

The mechanism of coal and gas outburst disasters is perplexing, and the evaluation methods of outburst disasters based on various sensitive indicators often have some imprecision and fuzziness. With the concept of accurate and intelligent mining in coal mines proposed in China, selecting quantifiable parameters for machine learning risk prediction can avoid the deviation caused by human subjectivity, and improve the accuracy of coal and gas outburst prediction. Aiming at the shortcomings of the support vector machine (SVM) such as low noise resistance and being prone to be influenced by parameters easily, this research proposed a prediction method based on a grey wolf optimizer to optimize the support vector machine (GWO-SVM). To coordinate the global and local optimization ability of the GWO, Tent Chaotic Mapping and DLH strategies were introduced to improve the optimization ability of the GWO and reduce the local optimal probability. The improved prediction model IGWO-SVM was used to predict the coal and gas outburst. The results showed that this model has faster training speed and higher classification prediction accuracy than the SVM and GWO-SVM models, which the accuracy rate reaching 100%. Finally, to obtain the correlation between the parameters of the coal and gas outburst prediction parameters, the random forest algorithm was used for training, and the three parameters with the highest feature importance were selected to rebuild the data set for machine learning. The accuracy of the IGWO-SVM outburst prediction model based on Random Forest was still 100%. Therefore, even if some prediction parameters are missing, the outburst can still be effectively predicted by using the RF-IGWO-SVM model, which is beneficial for the model application and underground safety management.

A novel intelligent risk prediction model for the effectiveness of CO2/N2–ECGD technology

“…When utilizing gas injection displacement and flow enhancement to extract gas, it is crucial to assess the suitability of the tested coal seam. On the basis of a statistical analysis of numerous safety risk examples, and taking into account the risk factors affecting coal mine gas safety, as well as relevant literature on the causes of coal mine safety risks, 7,[23][24][25][26][27][28][29][30][31][32][33] the potential influencing factors have been identified as follows: thickness of coal (a), Protodyakonov's coefficient (b), water content (c), ash content (d), porosity (e), permeability (f), gas content (g), gas pressure (h), gas saturation (i), Langmuir pressure constant (j), and Langmuir volume constant (k).…”

“…This technique bypasses the complex physical process of gas injection displacement, possesses robust computing and learning capabilities, and finds applications in intricate geological settings in coal mines, including situations, such as coal and gas outbursts, coal and rock strength, and the prediction of spontaneous combustion in goafs. 7 However, there is a noticeable lack of research on the prediction of displacement effects based on deep learning algorithms in the context of gas injection displacement technology as applied to coal seams. In the field of safety management research, several techniques have been employed for risk assessment, including empirical, 8,9 analytical, 10,11 numerical, [12][13][14] experimental, 15,16 intelligent, [17][18][19] and expert system 20 approaches.…”

“…Previous research, for example, Zhao et al, 22 aimed to investigate the use of ML for coal mine outburst prediction. To predict the risk of coal and gas eruption, Wang et al 7 established the improved grey wolf optimizer-SVM model. ML was used to study the gas occurrence pattern by Jiao et al 23 For the prediction of coal spontaneous combustion risk, Wang et al 24 proposed the SSA-CNN model.…”