Hierarchical Adaptive Transformer Framework for Modeling Abrupt Short-Term Fluctuations of Hazardous Gas Concentrations in Industrial Air

Abstract

Short-term prediction of hazardous gas concentrations is crucial for industrial air monitoring, but conventional approaches often fail to capture abrupt local fluctuations and nonlinear temporal dependencies, limiting prediction accuracy. To address these limitations, this study develops a multi-task residual Transformer-based framework for short-term concentration forecasting. First, historical high-frequency H2S measurements are processed using a sliding-window approach to form input sequences for the model. Next, a shared Transformer encoder extracts temporal features, while task-specific branches perform residual concentration prediction and concentration-based emission-state classification. Within this multi-task framework, an adaptive weighting mechanism emphasizes high-variation samples during training to improve sensitivity to rapid concentration changes. Experiments conducted on data from the South Coast Air Quality Management District demonstrate that, averaged over three random seeds, the model achieves an MAE of 0.133±0.001, an RMSE of 0.237±0.000, and an R2 of 0.810±0.001 for one-observation-step forecasting. These results show that the proposed framework effectively captures abrupt rises and peak concentrations, providing a reliable tool for industrial emission monitoring and early warning applications.