Accurate prediction of the coal spontaneous combustion hazard grades is of great significance to ensure the safe production of coal mines. However, traditional coal temperature prediction models have low accuracy and do not predict the coal spontaneous combustion hazard grades. In order to accurately predict coal spontaneous combustion hazard grades, a prediction model of coal spontaneous combustion based on principal component analysis (PCA), case-based reasoning (CBR), fuzzy clustering (FM), and the snake optimization (SO) algorithm was proposed in this manuscript. Firstly, based on the change rule of the concentration of signature gases in the process of coal warming, a new method of classifying the risk of spontaneous combustion of coal was established. Secondly, MeanRadius-SMOTE was adopted to balance the data structure. The weights of the prediction indicators were calculated through PCA to enhance the prediction precision of the CBR model. Then, by employing FM in the case base, the computational cost of CBR was reduced and its computational efficiency was improved. The SO algorithm was used to determine the hyperparameters in the PCA-FM-CBR model. In addition, multiple comparative experiments were conducted to verify the superiority of the model proposed in this manuscript. The results indicated that SO-PCA-FM-CBR possesses good prediction performance and also improves computational efficiency. Finally, the authors of this manuscript adopted the Random Balance Designs—Fourier Amplitude Sensitivity Test (RBD-FAST) to explain the output of the model and analyzed the global importance of input variables. The results demonstrated that CO is the most important variable affecting the coal spontaneous combustion hazard grades.

The heat dissipated from high geo-temperature underground surrounding rocks is the main heat source of working faces, while thermal water upwelling and trickling into the roadway will notably deteriorate the mine’s climate and thermal comfort. Predicting airflow temperature and relative humidity (RH) is conductive to intelligent control of air conditioning cooling and ventilation regulation. To accommodate this issue, an intelligent technique was proposed, integrating a genetic algorithm (GA) and long short-term memory (LSTM) based on rock temperature, inlet air temperature, water temperature, water flow rate, RH, and ventilation time. A total of 21 input features including over 200 pieces of data were collected from an independently developed modeling roadway to construct a dataset. Principal component analysis (PCA) was conducted to reduce features, and GA was used to tune the LSTM and PCA-LSTM architectures for best performance. The following research results were yielded. The proposed PCA-LSTM-GA model is more reliable and efficient than the single LSTM model or hybrid LSTM-GA model in predicting the air temperature $T_{f_{out}}$ and humidity ${RH}_{out}$ at the end of the water trickling roadway. The importance scores (ISs) indicate that $T_{f_{out}}$ is mainly influenced by the surrounding rock temperature (IS 0.661) and the inlet air temperature (IS 0.264). While ${RH}_{out}$ is primarily influenced by the rock temperature in the water trickling section (IS 0.577), the inlet air temperature (IS 0.187), and the trickling water temperature and flow rate (total IS 0.136), and it has an evident time effect. In addition, we developed relevant equipment and provided engineering practice methods to use the machine learning model. The proposed model, which can predict the mine microclimate, serves to facilitate coal and geothermal resource co-mining as well as thermal hazard control.