Machine learning models for predicting localized thermal resistance of firefighting gloves: A comparative study

Abstract

Optimizing thermal comfort for gloves requires precise predictions of localized thermal resistance (R_ct), yet traditional experimental methods are costly and limited in capturing complex interactions. This study developed a computational framework employing Bayesian-optimized machine learning models, including multiple linear regression (MLR), decision tree (DT), generalized additive model (GAM), support vector regression (SVR), and deep learning (DL), to predict localized R_ct of firefighting gloves. Utilizing 1680 measurements from firefighting gloves, predictive accuracy and stability were evaluated. DL demonstrated the highest predictive accuracy (R² = 0.924690, mean absolute error [MAE] = 0.009970), closely followed by SVR (R² = 0.913098, MAE = 0.011209) and GAM (R² = 0.909959, MAE = 0.011472), whereas DT and MLR exhibited lower accuracy. Response surface analysis revealed that GAM provides consistent and interpretable predictions in sparse data contexts; DL captures detailed, complex interactions but requires careful management of potential overfitting; SVR represents an intermediate choice, balancing nonlinear accuracy with smoothness but still vulnerable to occasional nonphysical predictions in sparse data scenarios. Key predictors were air layer thickness, glove thickness, wind speed, glove section, and surface area. Thermal resistance increased consistently with air layer thickness, whereas higher wind speeds reduced insulation. Predictions highlighted the little finger as the most critical region, with R_ct decreasing from 0.145 to 0.112 K m²/W as the wind speed increased from 0 to 2 m/s. Future studies should incorporate experimental validation under realistic firefighting scenarios, expand dataset diversity, and apply physically constrained models to enhance prediction robustness.