Interpretable Data-Driven Estimation and Uncertainty Quantification of Mine-Roadway Friction Resistance Coefficients for Ventilation-Parameter Identification

This study develops an interpretable point-and-interval prediction method for mine ventilation-parameter identification using limited field samples. Guided by ventilation-resistance theory, eight engineering attributes—roadway length, width, height, cross-sectional area, wetted perimeter, equivalent diameter, support type, and cross-sectional shape—were selected, and a prediction dataset was constructed from 260 field samples. The extended Random Feature Model (xRFM) establishes a nonlinear mapping, and the proposed Evolution-Path Guided Ivy Algorithm (EPG-IVY) optimizes hyperparameters within the training data. Prediction intervals are then constructed from out-of-fold residuals using Adaptive Bandwidth Kernel Density Estimation (ABKDE), while SHAP is used to analyze the model’s dependence on engineering features. With a fixed 7:3 split, EPG-IVY-xRFM achieved R2 = 0.9754, RMSE = 0.001600, and MAE = 0.001162. Repeated nested five-fold cross-validation yielded R2 = 0.9719 ± 0.0084, RMSE = 0.001675 ± 0.000267, and MAE = 0.001225 ± 0.000182, with lower RMSE and MAE than the optimized XGBoost and CatBoost baselines. External validation on 28 independent samples from Balasu Coal Mine yielded R2 = 0.890, RMSE = 0.007072, and MAE = 0.003873. The empirical coverage of the 90% ABKDE prediction interval was 0.8846, and its Wilson 95% confidence interval included the nominal coverage level. SHAP results identified support type, roadway width, and equivalent diameter as the main contributing factors in the current model. These results provide a data-driven framework for friction resistance coefficient identification with point prediction, uncertainty estimation, and feature attribution under limited field samples.