## 4 Experiments and Results Discussion
### Models Evaluation
#### 4.2.2 Comparison of Performance of Different Models  
The PLSR, SVR, standard 1D-CNN, and MDISE models were trained using the training set data collected by each filter channel, with the hyperparameters adjusted using the validation set data during the training process. To evaluate the gas concentration inversion performance of the models, the data from the test set with a blackbody temperature of 353 K were used for model validation. The performance of the different models is shown in Table 4, where \({R_{L}}^{2}\), \({R_{M}}^{2}\), and \({R_{H}}^{2}\) represent the fitting performance of the models in the low (10-30%), medium (40-60%), and high (70-90%) gas concentration intervals, respectively. The regression prediction scatter plots for each model in the 1 st and 2 nd filter channels are shown in Figures 12 and 13, respectively. From the results above, it can be observed that the MDISE model has the best performance. In the 1 st and 2 nd filter channels, the \(RMSE\) of the MDISE model decreases by 79.14%, 76.59%, and 69.80%, and 81.45%, 82.65%, and 74.01% compared to the PLSR, SVR, and standard 1D-CNN models, respectively. Additionally, the average \(R^{2}\) values \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**1 st Filter Channel**} & \multicolumn{4}{c}{**2 nd Filter Channel**} \\ \cline{2-9}  & \(RMSE\)/\% & \({R_{L}}^{2}\) & \({R_{M}}^{2}\) & \({R_{H}}^{2}\) & \(RMSE\)/\% & \({R_{L}}^{2}\) & \({R_{M}}^{2}\) & \({R_{H}}^{2}\) \\ \hline PLSR & 2.1689 & 0.9402 & 0.9284 & 0.9196 & 3.1053 & 0.8483 & 0.8706 & 0.8486 \\ SVR & 1.9328 & 0.9541 & 0.9333 & 0.9443 & 3.3184 & 0.8947 & 0.8712 & 0.7421 \\
1D-CNN & 1.4981 & 0.9864 & 0.9665 & 0.9464 & 2.2157 & 0.9606 & 0.9339 & 0.8861 \\ MDISE & 0.4524 & 0.9991 & 0.9989 & 0.9929 & 0.5759 & 0.9949 & 0.9954 & 0.9948 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the different models. Figure 12: Regression prediction scatter plots of **(a)** PLSR; **(b)** SVR; **(c)** 1D-CNN; and **(d)** MDISE models in 1 st filter channel at a blackbody temperature of 353 K.  
Figure 13: Regression prediction scatter plots of **(a)** PLSR; **(b)** SVR; **(c)** 1D-CNN; and **(d)** MDISE models in 2 nd filter channel at a blackbody temperature of 353 K.  
of the MDISE model in both channels across all concentration intervals reach 0.997 and 0.995, outperforming the PLSR, SVR, and standard 1D-CNN models. From the regression prediction scatter plots in Figures 12 and 13, it can also be observed that the concentration predictions of the MDISE model are closer to the true values. The main reasons for the performance differences between the models are as follows: The PLSR model constructs a linear relationship between the latent variables and gas concentration values, and although it can handle multicollinearity issues in spectral data, its ability to model the complex nonlinear relationship between the spectrum and concentration is limited. The SVR model, using the RBF kernel function, can handle nonlinear relationships but is sensitive to local feature changes and is easily affected by noise. The standard 1D-CNN model overcomes noise interference by stacking multiple convolutional layers, enabling it to extract complex feature information from the spectral data, and it uses the ReLU activation function for nonlinear mapping, learning the complex nonlinear relationship between spectrum and concentration. However, the standard 1D-CNN model has a single convolution kernel size in each convolutional layer, limiting the richness of the features extracted; also, it applies the same weight update method across all channels, neglecting the importance differences among different feature channels. In contrast, the MDISE model uses MD and 1D Inception modules to extract both shallow and deep features at multiple scales, fully extracting and utilizing effective information in the spectral data.