煤矿安全2025，Vol.56Issue(12)：58-68,11.DOI:10.13347/j.cnki.mkaq.20241129
基于多元融合算法的矿井油型气含量预测
Prediction of oil-type gas content in mines based on multivariate fusion algorithm
摘要
Abstract
In order to improve the prediction precision and accuracy of oil-type gas content in mines,we propose an accurate determ-ination method based on multivariate data fusion.Based on the main factors affecting the oil-type gas content,and 27 sets of actual measurement data from the mine were collected,and the XGBoost algorithm was used to screen out buried depth,roof and floor li-thology,fold and porosity as the key features which were standardized to ensure that the data with different magnitudes could be reasonably fused in the modeling process.Four classical machine learning algorithms,namely,Kriging interpolation,least squares support vector machine,multilayer perception and gradient boosted regression tree,were used for preliminary prediction,and com-parative analysis was carried out for the regression problem of oil-type gas content.The results show that the gradient boosting re-gression tree algorithm performs best in terms of prediction performance,with a coefficient of determination of 0.987,a normalized mean square error between 0.001 and 0.010,and a total information criterion between 0.019 and 0.046.The prediction accuracy is further improved by combining the Stacking algorithm.The Stacking method fuses the prediction results of multiple base learners as new features by using an improved whale optimization algorithm to optimize the weights of each base learner.In order to further im-prove the prediction ability of the model,a bidirectional long and short-term memory network is introduced,and the final fusion model is constructed through a meta-learning mechanism to deeply learn the prediction results of the base learners in order to cap-ture more complex nonlinear relationships and temporal information.The fusion model significantly outperforms the traditional single algorithm on the test set.The average absolute error of model prediction is 0.116 m3/t,the average value of the normalized mean square error is 0.006,the average value of the total information criterion is 0.004,and the coefficient of determination is higher than 0.98,which shows its high accuracy and stability in the prediction of oil-type gas content in mines.关键词
油型气含量/Stacking多元特征融合/XGBoost/IWOA-BiLSTM/预测模型Key words
oil-type gas content/Stacking multivariate feature fusion/XGBoost/IWOA-BiLSTM/prediction model分类
矿业与冶金引用本文复制引用
SU Jiahao,LIU Yang,SUN Liang,ZHAO Haibo,WANG Chunguang,TIAN Fuchao..基于多元融合算法的矿井油型气含量预测[J].煤矿安全,2025,56(12):58-68,11.基金项目
辽宁省自然科学基金资助项目(2023-MS-355,2022-KF-23-03) （2023-MS-355,2022-KF-23-03）
河南省瓦斯地质与瓦斯治理重点实验室2023年度开放基金资助项目(WS2023B06) （WS2023B06）
华能集团总部科技资助项目(HNKJ23-H18) （HNKJ23-H18）
