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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2406.13954 |
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| _version_ | 1866917699596058624 |
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| author | Liu, Haoxing Shen, Fangzhou and, Haoshen Qin Gao, Fanru |
| author_facet | Liu, Haoxing Shen, Fangzhou and, Haoshen Qin Gao, Fanru |
| contents | 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 characteris-tics of the aircraft and the sophistication of the fuselage structure, flight de-lays 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 backpropaga-tion 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_13954 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Research on Flight Accidents Prediction based Back Propagation Neural Network Liu, Haoxing Shen, Fangzhou and, Haoshen Qin Gao, Fanru Artificial Intelligence 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 characteris-tics of the aircraft and the sophistication of the fuselage structure, flight de-lays 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 backpropaga-tion 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. |
| title | Research on Flight Accidents Prediction based Back Propagation Neural Network |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2406.13954 |