A Risk Prediction Model of Hard Landing Based on Random Forest Algorithm

• Main Authors: Sun Ruishan, Li Chongfeng
• Format: Article
• Language: English
• Published: EDP Sciences 2021-01-01
• Series: E3S Web of Conferences
• Online Access: https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/33/e3sconf_aesee2021_02080.pdf

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.