A LSTM and Cost-Sensitive Learning-Based Real-Time Warning for Civil
  Aviation Over-limit
Yiming Bian
The issue of over-limit during passenger aircraft flights has drawn
increasing attention in civil aviation due to its potential safety risks. To
address this issue, real-time automated warning systems are essential. In this
study, a real-time warning model for civil aviation over-limit is proposed
based on QAR data monitoring. Firstly, highly correlated attributes to
over-limit are extracted from a vast QAR dataset using the Spearman rank
correlation coefficient. Because flight over-limit poses a binary
classification problem with unbalanced samples, this paper incorporates
cost-sensitive learning in the LSTM model. Finally, the time step length,
number of LSTM cells, and learning rate in the LSTM model are optimized using a
grid search approach. The model is trained on a real dataset, and its
performance is evaluated on a validation set. The experimental results show
that the proposed model achieves an F1 score of 0.991 and an accuracy of 0.978,
indicating its effectiveness in real-time warning of civil aviation over-limit.