## 2 Dataset Preparation
### Dataset Used  
The values of input and output variables of the machine learning model were acquired with the EAHMT. The arrangement of sensors is shown in Figure 3c. The experiment of each group had three stages: in Stage 1 (\(\sim\)24 h), the surrounding rocks were heated to a predetermined temperature, to simulate the virgin rock temperature before roadway excavation; in Stage 2 (\(\sim\)2 h), ventilation was maintained, to cause thermal disturbance in surrounding rocks until heat balance was reached between the surrounding rocks and airflow; and in Stage 3 (\(\sim\)3 h): supply of thermal water was maintained until heat and humidity balance was reached among the surrounding rocks, airflow, and fracture water. The dataset used in this study was from Stage 3, in which the thermal water trickling caused the change of the airflow microclimate to be so complex that it was difficult to predict with conventional theoretical models. The output variables of the model were the airflow humidity \(RH_{out}\) and temperature \(T_{feat}\) at the end of the roadway with water trickling. To better predict the output values, the input variables included 21 features, including rock temperature \(T\) (16 measuring points), trickling water temperature \(T_{w}\), trickling water flow rate \(Q_{w}\), inlet airflow humidity \(RH_{in}\), inlet airflow temperature \(T_{fn}\), and ventilation time \(\tau\). \(T\) and \(\tau\): The temperature of the rock not undergoing engineering disturbance in strata is called virgin rock temperature \(T_{0}\). Figure 4 shows the temperature variation of surrounding rock in cylinder a in Stage 2. After roadway excavation, the roadway wall temperature \(T_{a_{1}}\) fell sharply as ventilation started. Temperature difference occurred in the surrounding rocks, so the heat conduction process occurred accordingly, and the thermal disturbance scope in the surrounding rocks was enlarged gradually over time \(\tau\). Finally, the heat flux in surrounding rocks tended to be consistent, indicating re-balance of temperature field of surrounding rocks after disturbance. Therefore, during heat dissipation from surrounding rocks to the airflow, the temperatures at 16 measuring points for surrounding rocks in Figure 3c exhibited different variation characteristics with the ventilation time \(\tau\), in other words, this feature also reflected the time of ventilation and that of heat dissipation from surrounding rocks. \(T_{fin}\) and \(RH_{in}\): The airflow exhibits heat and humidity accumulation effects during long-distance transport. Hence, the airflow humidity and temperature at the inlet are of important significance for prediction of airflow humidity and temperature at the end. At the same water vapor content, \(RH\) is related to temperature. The higher the temperature is, the greater the saturated humidity ratio in the air will be, which means that the stronger the hygroscopicity of the air will be. \(T_{w}\) and \(Q_{w}\): Different upwelling water temperatures and flow rates directly influence the humidity and temperature of airflow passing through the water upwelling section. For  
Figure 4: Dynamic variation curves of dimensionless temperature at various points of surrounding rocks in the radial direction with time (note: \(T_{f}\) denotes the airflow temperature). example, at high upwelling water temperature, the humidity ratio variation rates at various points after the water upwelling section make it difficult to achieve synchronism at the early stage. In Sanhejian Coal Mine, the water upwelling flow rate is normally not more than 3 m\({}^{3}\)/h within 16 m, and it should not be more than 250 mL/min in the similarity ratio conversion experiment. The Ordovician limestone water temperature in Sanhejian Coal Mine is 50 \({}^{\circ}\)C, the maximum upwelling water temperature in China's coal mines is 60-70 \({}^{\circ}\)C (for example, the temperature is 65 \({}^{\circ}\)C in Pingdingshan No.