## 4 Experiments and Results Discussion
### Models Evaluation
#### 4.2.2 Comparison of Performance of Different Models
Additionally, by using the SE module, the channel weights are adaptively calibrated, enhancing the focus on important wavelengths while suppressing redundant features, thereby improving the model's gas concentration inversion performance. The experimental results indicate that, under two filter channels with distinct CO\({}_{2}\) spectral characteristics, the MDISE model achieves high concentration prediction accuracy under all experimental conditions, demonstrating its broad applicability. The accuracy of gas concentration inversion mainly depends on the model's inversion performance, while the computational efficiency of the model fundamentally determines the feasibility of real-time monitoring applications. Table 5 presents the testing times for different models on 1800 gas absorbance spectra under two filter channels. The PLSR model, with its relatively simple structure, has the shortest average testing time for both filter channels, at only 0.0019 s; however, its prediction results are not optimal. The SVR model, with higher computational complexity, requires significant computational time and storage resources, especially for large datasets, with an average testing time greater than 2 s. The MDISE model improves computational efficiency by using 1 \(\times\) 1 convolution layers in both the MD module and the 1D Inception module to reduce the number of input feature channels. Additionally, the 1D Inception module uses two series-connected 3 \(\times\) 1 convolution layers instead of a single 5 \(\times\) 1 convolution layer to further reduce feature dimensionality. Compared to the SVR and 1D-CNN models, the MDISE model reduces the average testing time by 93.46% and 48.45%, respectively, to just 0.1352 s, making it capable of meeting the real-time requirements of most application scenarios. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & **1 st Filter Channel Testing** & **2 nd Filter Channel Testing** \\  & **Time/s** & **Time/s** \\ \hline PLSR & 0.0028 & 0.0010 \\ SVR & 2.0884 & 2.0438 \\
1D-CNN & 0.2904 & 0.2341 \\ MDISE & 0.1455 & 0.1249 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Testing time of the different models. Assessing the uncertainty in concentration inversion is essential for evaluating the model's reliability and effectiveness. The MDISE model's uncertainty was quantified using two metrics: mean absolute error (_MAE_) and prediction standard error (_PSE_), as shown in Table 6. In the medium (40-60%) concentration intervals, the _MAE_ is close to 0.12%, demonstrating higher prediction accuracy compared to the low (10-30%) and high (70-90%) concentration intervals. The model's errors and uncertainty are larger in these extreme concentration intervals, due to lower spectral SNR at low concentrations and nonlinear shifts caused by saturation absorption at high concentrations. Nevertheless, the _MAE_ is consistently below 0.24%, and the _PSE_ does not exceed 0.65%, confirming that the MDISE model delivers high accuracy and low uncertainty across all concentration intervals, meeting the requirements for high-precision and stable gas concentration inversion. The MDISE model's concentration inversion performance not only surpasses traditional spectral quantitative analysis methods but also demonstrates outstanding results when compared with the latest research in related fields. Although these studies utilize different optical instruments for various target gases, the same evaluation metrics can be applied to compare performance between models. In the 2022 study by [PERSON] et al. (2024) and the 2024 study by [PERSON] et al. (2020), traditional PLSR models were used for infrared gas spectral concentration inversion, with predicted \(R^{2}\) values of 0.9951 and 0.9983, respectively, both of which are higher than the concentration inversion accuracy of the PLSR model in this paper. This indicates that the spectra collected in this study have poorer consistency, making concentration inversion more challenging.