###### Abstract  
Gas source localization is vital in emergency scenarios to enable swift and effective responses. In this study, we introduce a gas source localization model leveraging the video vision transformer (ViViT). Utilizing synthetic time series diffusion data, the source grid is predicted by classifying the grid with the highest probability of gas occurrence within the diffusion data coverage. Through extensive experimentation using the NBC-RANS simulator, we generate large datasets of gas diffusion under varied experimental conditions and meteorological environments, enabling comprehensive model training and evaluation. Our findings demonstrate that the ViViT outperforms other deep learning models in processing time series gas data, showcasing a superior estimation performance. Leveraging a transformer architecture, the ViViT exhibits a robust classification performance even in scenarios influenced by weather conditions or incomplete observations. Furthermore, we conduct an analysis of accuracy and parameter count across various input sequence lengths, revealing the ability of the ViViT to maintain high computational efficiency while achieving accurate source localization. These results underscore the effectiveness of the ViViT as a model for gas source localization, particularly in situations demanding a rapid response in real-world environments, such as gas leaks or attacks. Keywords:chemical gas sensing; gas source localization; gas diffusion; deep neural network; transformer +
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diffusion patterns and the complex atmospheric conditions that influence their dispersion [3; 6; 10]. In response to these challenges, studies on gas source localization within static sensor networks have taken diverse approaches. Foremost among these are optimization methodologies and probabilistic approaches based on Bayesian inference. Optimization methods [11; 12] encompass gradient-based techniques that leverage various adaptations of least squares methodologies, representing a field ripe with substantial research advancements. Additionally, direct search methods [13] have made notable progress in this domain. Furthermore, endeavors employing genetic algorithms [14; 15] have led to significant breakthroughs. Probabilistic methodologies employ techniques such as Markov chain Monte Carlo [16], sequential Monte Carlo [17], differential evolution Monte Carlo [18], and polynomial chaos quadrature [19], demonstrating notable advancements. Recent advancements have seen the emergence of approaches employing machine learning models such as support vector machine and kernel ridge regression for gas source localization [4]. Moreover, active research endeavors are underway exploring methods utilizing deep neural networks, renowned for their efficacy in processing complex data, including time series data [20]. [PERSON] et al. [21] employed a deep neural network (DNN) to estimate gas source locations in 2D coordinates from sensor array data. [PERSON] et al. [22] utilized DNN and random forest classifiers to track gas sources using data generated from computational fluid dynamics (CFD) simulations. [PERSON] et al. [5] implemented convolutional long short-term memory neural network (CNN-LSTM) architectures to extract temporal and spatial features from gas concentration data collected from the real environment, enabling the estimation of gas source locations in static sensor networks amidst varying air conditions. Similarly, [PERSON] et al. [23] leveraged a long short-term memory deep neural network (LSTM-DNN) to estimate gas source locations using gas sensor arrays and anemometers. [PERSON] et al. [24] proposed a model integrating feedforward neural networks and recurrent neural networks with long short-term memory (LSTM-RNN) to predict gas source locations using data derived from CFD simulations.