Machine learning based prediction model and interpretable analysis of airflow temperature in water dropping shaft

Accurate prediction of the airflow temperature in the mine water dropping shaft is crucial for scientifically guiding the prevention and control of thermal hazards in mines. To improve the precision, stability, and interpretability of the prediction model for the airflow temperature in mine drainage shafts, combined with relevant literature research and the actual characteristics of mine airflow temperature, Pearson correlation analysis was introduced to screen the characteristic variables. The parameters of the LSBoost model were optimized using the WOS model, and a mine drainage shaft airflow temperature prediction model based on WOS-LSBoost was established. Under the same sample set conditions, compare the prediction precision of the five models with the established DT model, RF model, LSBoost model, and SVM model, and conduct interpretability analysis on the prediction models based on the SHAP analysis method. Research has demonstrated that the WOS-LSBoost model has the best predictive property, stronger generalization ability, and can effectively improve the precision of wind temperature prediction; the temperature of the wellhead airflow has the largest effect on the prediction model, while the surface pressure has the smallest effect on the prediction model; This research can offer scientific reference for the prevention and control of thermal hazards in mines.