重庆大学学报2026，Vol.49Issue(2)：34-45,12.DOI:10.11835/j.issn.1000-582X.2024.279
基于机器学习的煤自燃倾向性预测比较分析
A comparative analysis of coal spontaneous combustion tendency prediction based on machine learning
摘要
Abstract
To develop a high-performance model for predicting the spontaneous combustion tendency of coal,on the basis of multiple gas indicators and industrial analysis parameters,four machine learning approaches(random forest,neural network,support vector machine,and Stacking ensemble)were used to predict spontaneous combustion temperature and natural ignition period,thereby evaluating coal spontaneous combustion risk.The findings indicate that the Stacking ensemble model exhibits superior generalization capability.Furthermore,feature importance analysis reveals that volatile matter and ethylene are the most influential predictors for natural ignition period and spontaneous combustion temperature,respectively.Model performance evaluation suggests that increasing data volume significantly enhances the predictive generalization of all four methods for spontaneous combustion temperature.However,expanding data alone yields only marginal improvement in predicting the natural ignition period.Enhancing feature representation is therefore necessary to further improve model performance.关键词
煤自燃倾向性/机器学习/煤自燃温度预测/煤自然发火期Key words
coal spontaneous combustion tendency/machine learning/coal temperature prediction/spontaneous combustion period of coal分类
资源环境引用本文复制引用
邹佩喆,叶于欣,梁晓瑜,韩超..基于机器学习的煤自燃倾向性预测比较分析[J].重庆大学学报,2026,49(2):34-45,12.基金项目
浙江省自然科学基金面上项目(LY18E040001). Supported by General Program of Zhejiang Provincial Natural Science Foundation of China(LY18E040001). （LY18E040001）
