A Dual-Model Framework with Gramian Angular Field and Spatio-Temporal Attention for Rapid Gas Identification and Concentration Prediction

Rapid and accurate gas identification and concentration prediction are of critical importance for industrial safety, medical diagnostics, and environmental monitoring. However, signal distortion in complex environments and feature loss during data processing often degrade prediction accuracy and response speed. To address these challenges, this study proposes a dual-model framework for electronic nose systems. A gas classification model transforms time-series sensor data into two-dimensional feature maps using a composite Gramian Angular Field representation and end-to-end classification using a convolutional neural network (CNN). A gas concentration prediction model integrates a multi-branch attention mechanism, a CNN, and a bidirectional gated recurrent unit to capture spatial–temporal dependencies. A cascaded identification–prediction scheme is further developed to mitigate data distribution heterogeneity and enhance model robustness. The proposed method supports both single-label and multi-label tasks and exhibits strong adaptability under complex conditions, including low concentrations, varying humidity, and gas mixtures. Validation on public and laboratory-collected datasets demonstrates that, using only initial response-stage data, the classification model achieves 100% identification accuracy, while the prediction model attains R2 > 0.99 for the majority of target gases. These results confirm that the proposed framework provides an efficient and robust solution for rapid qualitative identification and quantitative prediction in electronic nose systems.