Predicting the diffusion rule of toxic gas plays a distinctly important role in emergency capability assessment and rescue work. Among diffusion prediction models, the traditional artificial neural network has exhibited excellent performance not only in prediction accuracy but also in calculation time. Nevertheless, with the continuous development of deep learning and data science, some new prediction models based on deep learning algorithms have been shown to be more advantageous because their structure can better discover internal laws and external connections between input data and output data. The long short-term memory (LSTM) network is a kind of deep learning neural network that has demonstrated outstanding achievements in many prediction fields. This paper applies the LSTM network directly to the prediction of toxic gas diffusion and uses the Project Prairie Grass dataset to conduct experiments. Compared with the Gaussian diffusion model, support vector machine (SVM) model, and back propagation (BP) network model, the LSTM model of deep learning has higher prediction accuracy (especially for the prediction at the point of high concentration values) while avoiding the occurrence of negative concentration values and overfitting problems found in traditional artificial neural network models.

Aiming at the complex airport environment and the large scale transformation of aircraft, a target detection algorithm based on YOLOv4 is designed. Based on the YOLOv4 algorithm structure, a feature fusion scale is added to the original 3 detection scales, which has the ability to obtain smaller target information. Simultaneously, the residual network structure is simplified, and a certain amount of calculation is reduced. In order to optimize the loss, the regression loss function CIoU is introduced, which accelerates the convergence of the network. The test results show that the algorithm of this article can reach a prediction accuracy of 93.31% for airplane target, a recall rate of 83.92%, a mean average accuracy of 91.02%, which is 2.49% higher than YOLOv4, which improves the recognition accuracy of the model in the airport environment.