## 3 Results
### Sequence Length Variants  
We vary the sequence length \(T\in\{8,\,10,\,15,\,20,\,25\}\) and analyze its impact on the performance of the models for gas source localization. For \(T=20\) and \(T=25\), CNN-LSTM and 3D-CNN are trained for 500 epochs. Given that the number of parameters of these models increases with the number of input frames, we extend the training epochs to ensure convergence during the learning process. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **f1-Score** & **Precision** & **Recall** & **Accuracy** & **Average Error Distance [m]** & **Params [M]** \\ \hline CNN-LSTM & 0.9110 & 0.9189 & 0.9134 & 0.9217 & 17.7394 & 9.86 \\
3D-CNN & 0.8102 & 0.8298 & 0.8163 & 0.8456 & 37.1616 & 6.74 \\ ViViT & 0.9363 & 0.9413 & 0.9383 & 0.9478 & 12.6894 & 2.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance evaluation scores of models on test data. Figure 5 illustrates the accuracy and number of parameters of the models with changes in sequence length. It is evident from Figure 5a that all models tend to improve their performance as the number of input frames increases. The ViViT consistently demonstrates the highest performance across all sequence lengths. Even in the \(T=8\) condition, utilizing the fewest frames, the ViViT exhibits superior performance compared to all the other length conditions of the other models. Using a larger number of input frames allows for more information utilization and potentially a higher performance. However, in practical scenarios, there is a trade off in data acquisition time. To respond swiftly to gas leaks or  
Figure 4: Visualization of three input data with different conditions and the results of source localization compared with ground truth; (**a**) input data and prediction results under minimal weather influence; (**b**) input data and prediction results under significant weather impact; (**c**) input data and prediction results under adverse weather conditions with partial measurement loss. incidents, a model capable of accurate prediction with a limited number of frames is crucial. Thus, the ability of the ViViT to achieve accurate source localization with a small number of frames offers a significant advantage in real-time applications. While the performance of CNN-LSTM improves with increasing frames, its accuracy gradually converges, showing a similar performance between the \(T=20\) and \(T=25\) conditions. 3D-CNN consistently exhibits a lower performance across all sequence lengths. We evaluate the computational efficiency of the transformer-based model structure in Figure 5b. The transformer architecture used in the ViViT ensures that the number of parameters to be learned in the encoder or classifier remains constant even as the number of input frames increases. Although the parameters for \(z_{cls}\) and positional embedding \(\mathbf{p}\) increase with the sequence length, they contribute only a fraction of the total number of parameters. In contrast, CNN-LSTM and 3D-CNN exhibit linear increases in parameters proportional to the number of input frames. The parameters of CNN-LSTM grow with the size of the hidden state of the LSTM and the number of nodes in the first layer of the DNN block. Furthermore, the sequential computation of LSTM outputs for each frame adds computational inefficiency. Similarly, the parameter increase in 3D-CNN is proportional to the product of the number of filters in the last convolutional layer, the dimensions of the output tensor of the last convolutional layer, and the number of nodes in the following dense layer. These models, with their larger parameter counts, demand extensive training and computational resources, making real-world deployment challenging. Even with increased epochs, CNN-LSTM and 3D-CNN fail to surpass the performance of the ViViT, which is particularly evident in the \(T=20\) and \(T=25\) conditions. Notably, the excessive parameters of 3D-CNN hinder training convergence in the \(T=20\) and \(T=25\) conditions, resulting in a slight performance decrease.