Issue |
E3S Web Conf.
Volume 257, 2021
5th International Workshop on Advances in Energy Science and Environment Engineering (AESEE 2021)
|
|
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Article Number | 02080 | |
Number of page(s) | 6 | |
Section | Research on Energy Consumption and Energy Industry Benefit | |
DOI | https://doi.org/10.1051/e3sconf/202125702080 | |
Published online | 12 May 2021 |
A Risk Prediction Model of Hard Landing Based on Random Forest Algorithm
1
Flight technology collage, Civil Aviation University of China, Tianjin, China
2
Economics and Management College, Civil Aviation University of China, Tianjin, China
a e-mail: sunrsh@hotmail.com
b* Corresponding author: 1049412572@qq.com
Landing safety is a hot issue in civil aviation safety management. In order to fully mine the influence factors of hard landing in flight data and effectively predict the risk of hard landing, the random forest algorithm was introduced. Firstly, this paper qualitatively analyzed the influence factors of hard landing, and chose the features of the model based on the flight data. Secondly, this paper gives a quantitative analysis method of the importance of features based on Gini index. Finally, for the dataset of hard landing was class-imbalanced, the model was training based on SMOTE method. Then, the random forests classifier was built and verified by real flight data. The results showed that the recall rate of the model was 85.50%. The model can not only effectively prevent the occurrence of hard landing, but also provide a method reference for airlines to apply data mining to improve the ability of flight events management.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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