Prediction of coal spontaneous combustion temperature under variable moisture contents: A study based on the random forest model optimized by the sparrow search algorithm
Wu C. Wang G. Song T. Xia T. Niu Y. Guo F. Hou P. Mukhammedrakhym R.
15 March 2026Institution of Chemical Engineers
Process Safety and Environmental Protection
2026#209
For the precise prediction of coal spontaneous combustion (CSC) temperature and effectively prevent coal mine fires, this study proposes a temperature prediction model integrating the Sparrow Search Algorithm (SSA) and Random Forest (RF). Firstly, the gas production characteristics during CSC were analyzed via coal temperature-programmed experiments, and the correlation intensity between temperature and indicator gases at each stage was quantified using the grey relational analysis method. Secondly, SSA was applied to optimize the hyperparameters of the RF model, thus constructing the SSA-RF CSC temperature prediction model. Under the same experimental conditions, the prediction performance of the proposed model was compared with that of five other models. In addition, the applicability of the model was verified using field data collected by the borehole bundle monitoring system. The results show that moisture content exerts a dual effect on the CSC process, an appropriate amount of moisture can promote CSC, while excessively high moisture content will inhibit this process. The mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) of the SSA-RF model are 1.63 °C, 2.64 °C and 0.9974, respectively, indicating that its prediction accuracy is superior to that of the other five comparative models. Meanwhile, the results of feature importance evaluation of the SSA-RF model are highly consistent with those of the grey relational analysis, which verifies the reliability of the model in screening key indicators. Further verification with field data shows that the SSA-RF model still maintains high prediction accuracy, with MAE, RMSE and R2 values of 0.35 °C, 0.45 °C and 0.9898, respectively, demonstrating good engineering applicability.
Coal spontaneous combustion , Different moisture contents , Random forest , Sparrow search algorithm , Temperature prediction
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Carbon neutrality institute, China University of Mining and Technology, Xuzhou, 221116, China
School of Low Carbon Energy and Power Engineering, China University of Mining and Technology, Xuzhou, 221116, China
Shaanxi Xiaobaodang Mining Co., Ltd., Yulin, 719000, China
Shaanxi Coal Group Shenmu Ningtiaota Mining Co., Ltd, Yulin, 719000, China
School of Civil Engineering, Wuhan University, Wuhan, 430072, China
Mining Faculty, Abylkas Saginov Karaganda Technical University, Karaganda, 100027, Kazakhstan
Carbon neutrality institute
School of Low Carbon Energy and Power Engineering
Shaanxi Xiaobaodang Mining Co.
Shaanxi Coal Group Shenmu Ningtiaota Mining Co.
School of Civil Engineering
Mining Faculty
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Книга Публикация научной статьи Волощук 2026 Book Publication of a scientific article 2026