Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87035-2 |
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| _version_ | 1832585763096625152 |
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| author | Hui Zhuo Tongren Li Wei Lu Qingsong Zhang Lingyun Ji Jinliang Li |
| author_facet | Hui Zhuo Tongren Li Wei Lu Qingsong Zhang Lingyun Ji Jinliang Li |
| author_sort | Hui Zhuo |
| collection | DOAJ |
| description | Abstract 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 O2, CO, CO2, C2H4, C3H8, C3H8/CH4, C2H4/CH4, C2H4/C3H8, CO2/CO, and CO/O2 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. |
| format | Article |
| id | doaj-art-2fbb0b8d7aad4d7d93e00d1f04037ef6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2fbb0b8d7aad4d7d93e00d1f04037ef62025-01-26T12:30:43ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-025-87035-2Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithmHui Zhuo0Tongren Li1Wei Lu2Qingsong Zhang3Lingyun Ji4Jinliang Li5College of Safety Science and Engineering, Anhui University of Science and TechnologyCollege of Safety Science and Engineering, Anhui University of Science and TechnologyCollege of Safety Science and Engineering, Anhui University of Science and TechnologyCollege of Safety Science and Engineering, Anhui University of Science and TechnologyCollege of Safety Science and Engineering, Anhui University of Science and TechnologyCollege of Safety Science and Engineering, Anhui University of Science and TechnologyAbstract 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 O2, CO, CO2, C2H4, C3H8, C3H8/CH4, C2H4/CH4, C2H4/C3H8, CO2/CO, and CO/O2 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.https://doi.org/10.1038/s41598-025-87035-2Coal spontaneous combustionPSO-XGBoost modelParticle swarm optimizationFeature selectionTenfold cross-validation |
| spellingShingle | Hui Zhuo Tongren Li Wei Lu Qingsong Zhang Lingyun Ji Jinliang Li Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm Scientific Reports Coal spontaneous combustion PSO-XGBoost model Particle swarm optimization Feature selection Tenfold cross-validation |
| title | Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm |
| title_full | Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm |
| title_fullStr | Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm |
| title_full_unstemmed | Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm |
| title_short | Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm |
| title_sort | prediction model for spontaneous combustion temperature of coal based on pso xgboost algorithm |
| topic | Coal spontaneous combustion PSO-XGBoost model Particle swarm optimization Feature selection Tenfold cross-validation |
| url | https://doi.org/10.1038/s41598-025-87035-2 |
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