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<p>Underground coal fires can cause environmental damage and resource wastage. Accurately predicting the combustion state is critical for assessing their hazard levels and implementing control measures. Underground coal fires produce radon gas, which serves as an effective indicator for predicting the coal fire. However, traditional radon measurement is time-consuming and costly. This study proposes a machine learning (ML)-based prediction method that correlates surface radon concentration with atmospheric environmental factors. Four ML models—ridge regression, random forest, gradient boosting, and extra trees—were applied to predict surface radon concentration anomalies caused by underground coal fires. In total, 216 samples were collected from the Haizhou open-pit mine fire area, including four key variables: ambient temperature, ambient pressure, relative humidity (RH), and wind velocity. The results demonstrated that the gradient boosting model exhibited exceptional fit and robust generalizability, achieving R<sup>2</sup> of 0.981, RMSE of 170.29, and MAE of 124.69 on the testing dataset. Sensitivity analysis revealed that RH was the most influential factors. The gradient boosting model proved to exhibit high stability and accuracy in predicting radon concentrations over different time durations. The application of this model in radon concentration prediction provides significant support for forecasting underground coal fires and expands new directions for the application of ML in environmental fields.</p>

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Machine Learning-Based Prediction of Radon Emissions from Underground Coal Fires Based on Atmospheric Changes

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Hemeng Zhang,
  • Zhiyu Zhang,
  • Hung Vo Thanh,
  • Shuliang Jia,
  • Zhenxue Dai,
  • Yan Zhang,
  • Yongjun Wang

摘要

Underground coal fires can cause environmental damage and resource wastage. Accurately predicting the combustion state is critical for assessing their hazard levels and implementing control measures. Underground coal fires produce radon gas, which serves as an effective indicator for predicting the coal fire. However, traditional radon measurement is time-consuming and costly. This study proposes a machine learning (ML)-based prediction method that correlates surface radon concentration with atmospheric environmental factors. Four ML models—ridge regression, random forest, gradient boosting, and extra trees—were applied to predict surface radon concentration anomalies caused by underground coal fires. In total, 216 samples were collected from the Haizhou open-pit mine fire area, including four key variables: ambient temperature, ambient pressure, relative humidity (RH), and wind velocity. The results demonstrated that the gradient boosting model exhibited exceptional fit and robust generalizability, achieving R2 of 0.981, RMSE of 170.29, and MAE of 124.69 on the testing dataset. Sensitivity analysis revealed that RH was the most influential factors. The gradient boosting model proved to exhibit high stability and accuracy in predicting radon concentrations over different time durations. The application of this model in radon concentration prediction provides significant support for forecasting underground coal fires and expands new directions for the application of ML in environmental fields.