Coal and gas outburst prediction model based on principal component analysis and improved support vector machine

作     者：Chaojun Fan Xinfeng Lai Haiou Wen Lei Yang 

作者机构：College of MiningLiaoning Technical UniversityFuxin123000China 

出 版 物：《Geohazard Mechanics》 (岩土灾变力学（英文）)

年 卷 期：2023年第1卷第4期

页      面：319-324页

学科分类：0819[工学-矿业工程] 081903[工学-安全技术及工程] 08[工学] 

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) 

主　　题：Coal and gas outburst Risk prediction Principal component analysis(PCA) Firefly algorithm(FA) Support vector machine(SVM) 

摘      要：In order to predict the coal outburst risk quickly and accurately,a PCA-FA-SVM based coal and gas outburst risk prediction model was *** 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 *** rate,recall rate,Macro-F1 and model prediction time were used as evaluation *** results show that:Principal component analysis improves the prediction efficiency and accuracy of FA-SVM *** 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 *** with other models,The comprehensive performance of PCA-FA-SVM model is better.