# A LSTM and Cost-Sensitive Learning-Based Real-Time Warning for Civil Aviation Over-limit

arXiv: https://arxiv.org/abs/2305.04618 · HTML: https://mlsanity.com/paper/2305.04618/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 1 of 1)

- [Cost-aware Pre-training for Multiclass Cost-sensitive Deep Learning](https://mlsanity.com/paper/1511.09337/) (2015)

## Abstract (arXiv)

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.