## 3 Results
### Performance Analysis  
Table 1 presents the performance metrics and the number of parameters for each model when the input sequence length is \(T=10\). The ViViT achieves metrics of 0.93 or higher across all classification performance metrics, demonstrating the highest accuracy in source grid prediction. Additionally, it exhibits the lowest error distance, with an average distance of 12.689 m between the predicted source location and the actual one. CNN-LSTM shows a classification performance of 0.91 or higher and an average error distance of 17.7394 m, ranking second in performance. 3D-CNN exhibits metrics of 0.81 or higher, showing the  
lowest performance among the comparison groups, with an average error distance of 37.1616 m, approximately three times larger than that of the ViViT. The ViViT comprises 2.10 million parameters, which is approximately 4.6 times fewer than CNN-LSTM and 3.3 times fewer than 3D-CNN. Despite the potential for learning more complex nonlinear relationships with a larger number of parameters, the ViViT demonstrates a superior performance with the fewest parameters.  
Figure 4 illustrates three samples from the test data along with the predicted gas source locations by each model. For each sample, the results display 10 data sequences used as the input on the left and the prediction results of each model alongside the ground truth on the right. In Figure 4a, the gas distribution widens over time, indicating a spreading pattern observed across the entire area. Since the distribution of grids showing high concentrations in each frame does not change significantly, it can be seen that the influence of weather does not have a significant effect on gas diffusion, especially when the wind is weak. This sample not only allows for the observation of the entire distribution that changes over time but also confirms that all models accurately predict the source grid because the change in the concentration distribution is monotonic. In Figure 4b, the distribution of gas is observed to move to the bottom right over time, indicating a stronger influence of the wind compared to Figure 4a. Although the results of CNN-LSTM and 3D-CNN are predicted to be close to the actual source location, they do not accurately predict the source location when the gas distribution moves or changes due to the influence of weather. On the other hand, the ViViT accurately localizes the source grid, overcoming these difficulties. In the case of Figure 4c, the distribution of gas moves significantly to the left over time due to the influence of weather, and even in the last two frames, it extends beyond the detection area with no observable values. CNN-LSTM and 3D-CNN fail to accurately estimate the location of the source, with larger errors compared to Figure 4b. However, the ViViT accurately predicts the source grid even under such conditions. Through these examples, it is evident that the ViViT outperforms other models in gas source localization. The ViViT can accurately predict the grid of the source even if the distribution changes significantly over time due to the influence of weather, and even if the distribution is outside the area of interest or some observations are missing. Considering the limitation that it is difficult to respond when gases escape the designated area through diffusion, the ViViT offers an efficient solution to this challenge. The rationale behind the superior performance of the ViViT lies in its utilization of a transformer structure, enabling it to effectively acquire spatio-temporal features compared to other models for processing time series data. While training a model with a transformer structure demands a substantial dataset, such data can be readily acquired through simulations. Moreover, an alternative approach involves loading the parameters of a pretrained model and fine-tuning only some of the parameters with a small dataset.