Zhuo Hui, Li Tongren, Lu Wei, Zhang Qingsong, Ji Lingyun, Li Jinliang
College of Safety Science and Engineering, Anhui University of Science and Technology, Huainan, 232001, People's Republic of China.
Engineering Technology Research Centre for Safe and Efficient Coal Mining, Anhui University of Science and Technology, Huainan, 232001, People's Republic of China.
Sci Rep. 2025 Jan 22;15(1):2752. doi: 10.1038/s41598-025-87035-2.
The construction of a predictive model that accurately reflects the spontaneous combustion temperature of coal in goaf is fundamental to monitoring and early warning systems for thermodynamic disasters, including coal spontaneous combustion and gas explosions. In this paper, on the basis of programming temperature experiment and industrial analysis, 381 data sets of 9 coal types are established, and feature selection was executed through the utilization of the Pearson correlation coefficient, ultimately identifying O, CO, CO, CH, CH, CH/CH, CH/CH, CH/CH, CO/CO, and CO/O as input indicators for the prediction model. The chosen indicator data were divided into training and testing sets in a 4:1 ratio, the Particle Swarm Optimization (PSO) methodology was applied to optimize the parameters of the XGBoost regressor, and a universal PSO-XGBoost prediction model is proposed. A tenfold cross-validation method was employed to assess performance of PSO-XGBoost, PSO-RF, PSO-SVR, XGBoost, RF, and SVR models separately, the results underscored the superior predictive accuracy, robustness, fault tolerance, and universality of the PSO-XGBoost model.
构建一个能够准确反映采空区煤炭自燃温度的预测模型，对于包括煤炭自燃和瓦斯爆炸在内的热动力灾害监测与预警系统至关重要。本文在程序升温实验和工业分析的基础上，建立了9种煤种的381个数据集，并利用皮尔逊相关系数进行特征选择，最终确定O、CO、CO、CH、CH、CH/CH、CH/CH、CH/CH、CO/CO和CO/O作为预测模型的输入指标。将所选指标数据按4:1的比例分为训练集和测试集，应用粒子群优化（PSO）方法对XGBoost回归器的参数进行优化，提出了通用的PSO-XGBoost预测模型。采用十折交叉验证法分别评估PSO-XGBoost、PSO-RF、PSO-SVR、XGBoost、RF和SVR模型的性能，结果表明PSO-XGBoost模型具有更高的预测精度、鲁棒性、容错性和通用性。
