Research on Flight Accidents Prediction based Back Propagation Neural Network

Haoxing Liu; Fangzhou Shen; Haoshen Qin; Fanru Gao

doi:10.2991/978-94-6463-514-0_65

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Research on Flight Accidents Prediction based Back Propagation Neural Network

Authors

Haoxing Liu¹, Fangzhou Shen², Haoshen Qin³, Fanru Gao⁴^{, *}

¹Flight Department, Shanghai Jixiang Airlines Co., Ltd., Shanghai, 201101, China

²Department of Mathematics and Statistics, San Jose State University, San Jose, 95129, USA

³Department of Computer & Information Science & Engineering, Herbert Wertheim College of EngineeringUniversity of Florida, Gainesville, USA

⁴Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA

^*Corresponding author. Email: fxg149@case.edu

Corresponding Author

Fanru Gao

Available Online 28 September 2024.

DOI: 10.2991/978-94-6463-514-0_65 How to use a DOI?
Keywords: Flight accidents; Back-propagation neural network; Data processing; Prediction
Abstract: 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.
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
Series: Advances in Engineering Research
Publication Date: 28 September 2024
ISBN: 978-94-6463-514-0
ISSN: 2352-5401
DOI: 10.2991/978-94-6463-514-0_65 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Haoxing Liu
AU  - Fangzhou Shen
AU  - Haoshen Qin
AU  - Fanru Gao
PY  - 2024
DA  - 2024/09/28
TI  - Research on Flight Accidents Prediction based Back Propagation Neural Network
BT  - Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
PB  - Atlantis Press
SP  - 679
EP  - 685
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-514-0_65
DO  - 10.2991/978-94-6463-514-0_65
ID  - Liu2024
ER  -

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