Prediction of spontaneous combustion susceptibility of coal seams based on coal intrinsic properties using various machine learning tools

Spontaneous combustion of coal leading to mine fire is a major problem in most of the coal mining countries in the world. It causes major loss to the Indian economy. The liability of coal to spontaneous combustion varies from place to place and mainly depends on the coal intrinsic properties and other geo-mining factors. Hence, the prediction of spontaneous combustion susceptibility of coal is of utmost importance for preventing the risk of fire in coal mines and utility sectors. Machine learning tools are pivotal in system improvements in relation to the statistical analysis of experimental results. Wet oxidation potential (WOP) of coal determined in the laboratory is one of the most relied indices used for assessing the spontaneous combustion susceptibility of coal. In this study, multiple linear regression (MLR) and five different machine learning (ML) techniques, such as Support Vector Regression (SVR), Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting (GB) and Extreme Gradient Boost (XGB) algorithms, were used to predict the spontaneous combustion susceptibility (WOP) of coal seams based on the coal intrinsic properties. The results derived from the models were compared with the experimental data. The results indicated excellent prediction accuracy and ease of interpretation of tree-based ensemble algorithms, like Random Forest, Gradient Boosting and Extreme Gradient Boosting. The MLR exhibited the least while XGB demonstrated the highest predictive performance. The developed XGB achieved R2 of 0.9879, RMSE of 4.364 and VAF of 84.28%. In addition, the results of sensitivity analysis showed that the volatile matter is most sensitive to the changes in WOP of coal samples considered in the study. Thus, during spontaneous combustion modelling and simulation, volatile matter can be used as the most influential parameter for assessing the fire risk of the coal samples considered in the study. Further, the partial dependence analysis was done to interpret the complex relationships between the WOP and intrinsic properties of coal.