With the rapid development of civil aviation and the significant improvement of people’s living standards, taking an air plane has become a common and efficient way of travel. However, due to the flight characteristics of the aircraft and the sophistication of the fuselage structure, flight delays and flight accidents occur from time to time. In addition, the life risk factor brought by aircraft after an accident is also the highest among all means of transportation. In this work, a model based on back-propagation neural network was used to predict flight accidents. By collecting historical flight data, including a variety of factors such as meteorological conditions, aircraft technical condition, and pilot experience, we trained a backpropagation neural network model to identify potential accident risks. In the model design, a multi-layer perceptron structure is used to optimize the network performance by adjusting the number of hidden layer nodes and the learning rate. Experimental analysis shows that the model can effectively predict flight accidents with high accuracy and reliability.
Research on Flight Accidents Prediction based Back Propagation Neural Network
Advances in Engineering res
International Symposium on Traffic Transportation and Civil Architecture ; 2024 ; Tianjin, China June 21, 2024 - June 23, 2024
Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024) ; Chapter : 65 ; 679-685
2024-09-24
7 pages
Article/Chapter (Book)
Electronic Resource
English
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