湖北民族大学学报（自然科学版）2025，Vol.43Issue(1)：53-59,7.DOI:10.13501/j.cnki.42-1908/n.2025.03.008
基于DBSCAN-IHHO-SVM模型的煤与瓦斯突出预测
Coal and Gas Outburst Prediction Based on DBSCAN-IHHO-SVM Model
摘要
Abstract
The complexity of coal and gas outburst accidents and the low prediction accuracy caused by the difficulty of data acquisition were addressed by proposing the density-based spatial clustering of applications with noise-improved Harris hawks optimization-support vector machine(DBSCAN-IHHO-SVM)warning model.Firstly,gas content,gas pressure,coal seam porosity,and the coal seam robustness coefficient were selected as predictors,and missing values in the data were processed by mean filling.The amount of outburst data was expanded using a generative adversarial network(GAN).Secondly,DBSCAN was employed to identify potentially hazardous data from non-outburst data,which were then treated as new outburst data.Finally,the parameters of the SVM model adjusted by IHHO were introduced,and the processed data were fed into the IHHO-SVM model for predictive analysis.Compared with the original SVM model,the results showed that the overall prediction accuracy and hazardous data identification rate of DBSCAN-IHHO-SVM model were improved by 5.87%and 38.46%,respectively.When faced with limited outburst data samples,DBSCAN-IHHO-SVM model effectively mined the potential information of non-outburst data,achieving accurate early warning and offering new insights for research in this field.关键词
煤与瓦斯突出/预测/危险数据识别/数据扩充/IHHO/SVMKey words
coal and gas outburst/prediction/dangerous data identification/data expansion/IHHO/SVM分类
矿山工程引用本文复制引用
郑晓亮,王琦,来文豪,张贺,张玉婷..基于DBSCAN-IHHO-SVM模型的煤与瓦斯突出预测[J].湖北民族大学学报（自然科学版）,2025,43(1):53-59,7.基金项目
"十四五"重点研发计划资助项目(2023YFB321103) （2023YFB321103）
煤炭安全精准开采国家地方联合工程研究中心开放基金资助项目(EC2021003). （EC2021003）
