Nowadays, the quality of air in the environment has been impacted by the industry. It is important to make sure our ambient air especially in an indoor environment is clean from contaminating particles or harmful gases. Therefore, the air quality inside the indoor environment should be monitored regularly. One of the major problems, when a particular environment has been contaminated by harmful gases, is finding the source of the emission. If the indoor environment has been contaminated by a harmful source it should be instantly localized and eliminated to prevent severe casualties. In this paper, we propose the utilization of synthetic data generated by the Computational Fluid Dynamic (CFD) approach to train the Deep Neural Network (DNN) model called CFD-DNN to perform gas source localization in an indoor environment. The model is capable to locate the contaminated source within a small area of an indoor environment. A total of 361 datasets with different locations of contaminated source release have been obtained using the CFD approach. The obtained dataset was divided into training and testing datasets. The training dataset was used for the model training process while the testing dataset is fed into the model to test model reliability to predict the gas source location. The Euclidian distance equation was used to measure the distance error between the actual and predicted location of the source. The result shows that the model is capable to locate the gas source within a minimum and maximum error of 0.03m to 0.46m respectively.
