Currently,coal mining faces the uncertainty of the risk of coal and gas outbursts and inaccurate prediction results.Owing to this,an artificial immune algorithm(AIA)was developed for coal and gas outburst prediction b...Currently,coal mining faces the uncertainty of the risk of coal and gas outbursts and inaccurate prediction results.Owing to this,an artificial immune algorithm(AIA)was developed for coal and gas outburst prediction based on the Hamming distance(HD)calculation method of antibody and antigen affinity called the Hamming distance artificial intelligence algorithm(HDAIA).The correlation matrix of coal and gas outburst indicators was constructed using the interpolation function in the algorithm.The HD algorithm was used to obtain the affinity between the antibody and antigen,and the minimum HD was screened to obtain the prediction result.The collected dynamic data of the drilling cuttings gas desorption index Ki and the drilling cuttings weight S during the excavation process of the 11,192-working face of a coal mine in Guizhou Province,China,were used as prediction indices.The results indicate that the prediction result of the HDAIA for the risk of coal and gas outbursts is consistent with the actual risk of outbursts,and it has a good prediction of the risk of coal and gas outbursts.The HDAIA can be used as a novel method for predicting the risk of coal and gas outbursts.展开更多
In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data ...In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data samples,extract the principal components of the samples,use firefly algorithm(FA)to improve the support vector machine model,and compare and analyze the prediction results of PCA-FA-SVM model with BP model,FA-SVM model,FA-BP model and SVM model.Accuracy rate,recall rate,Macro-F1 and model prediction time were used as evaluation indexes.The results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model.The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962,recall rate is 0.955,Macro-F1 is 0.957,and model prediction time is 0.312s.Compared with other models,The comprehensive performance of PCA-FA-SVM model is better.展开更多
基金supported by the National Natural Science Foundation of China(Nos.51974120 and 52274196)。
文摘Currently,coal mining faces the uncertainty of the risk of coal and gas outbursts and inaccurate prediction results.Owing to this,an artificial immune algorithm(AIA)was developed for coal and gas outburst prediction based on the Hamming distance(HD)calculation method of antibody and antigen affinity called the Hamming distance artificial intelligence algorithm(HDAIA).The correlation matrix of coal and gas outburst indicators was constructed using the interpolation function in the algorithm.The HD algorithm was used to obtain the affinity between the antibody and antigen,and the minimum HD was screened to obtain the prediction result.The collected dynamic data of the drilling cuttings gas desorption index Ki and the drilling cuttings weight S during the excavation process of the 11,192-working face of a coal mine in Guizhou Province,China,were used as prediction indices.The results indicate that the prediction result of the HDAIA for the risk of coal and gas outbursts is consistent with the actual risk of outbursts,and it has a good prediction of the risk of coal and gas outbursts.The HDAIA can be used as a novel method for predicting the risk of coal and gas outbursts.
基金financially supported by the National Natural Science Foundation of China(52174117,52004117)Postdoctoral Science Foundation of China(2021T140290,2020M680975)Science and Technology Research Project of Liaoning Provincial Department of Education(LJ2020JCL005).
文摘In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was designed.Principal component analysis(PCA)was used to pre-process the original data samples,extract the principal components of the samples,use firefly algorithm(FA)to improve the support vector machine model,and compare and analyze the prediction results of PCA-FA-SVM model with BP model,FA-SVM model,FA-BP model and SVM model.Accuracy rate,recall rate,Macro-F1 and model prediction time were used as evaluation indexes.The results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM model.The accuracy rate of PCA-FA-SVM model predicting coal and gas outburst risk is 0.962,recall rate is 0.955,Macro-F1 is 0.957,and model prediction time is 0.312s.Compared with other models,The comprehensive performance of PCA-FA-SVM model is better.
