# Research on intelligent forecasts of flight actions based on the implemented bi-LSTM

## TL;DR

This paper uses a bi-LSTM algorithm to forecast flight actions from real flight data, aiming to improve the objectivity of flight training assessments.

## Contribution

The novel implementation of bi-LSTM for flight action forecasting with optimized parameters and high recognition accuracy.

## Key findings

- The model achieved over 85% accuracy in forecasting flight actions.
- Training duration was controlled between 1 to 3 hours per session.
- Inference time remained under 2 seconds per run.

## Abstract

Rapid identification of flight actions by utilizing flight data is more realistic so the quality of flight training can be objectively assessed. The bidirectional long short-term memory (bi-LSTM) algorithm is implemented to forecast the flight actions of aircraft. The dataset containing the flight actions is structured by collecting tagged flight data when real flight training is exercised. However, the dataset needs to be preprocessed and annotated with expert rules. One of the deep learning (DL) methods, called the bi-LSTM algorithm, is implemented to train and test, and the pivotal parameters of the algorithm are optimized. Finally, the constructed model is applied to forecast the flight actions of aircraft. The training’s accuracy and loss rates are computed. The duration is kept between 1 through 3 h per session. Thus, the development of training the model is continued until an accuracy rate above 85% is achieved. The word-run inference time is kept under 2 s. Finally, the proposed algorithm’s specific characteristics, which are short training time and high recognition accuracy, are achieved when complex rules and large sample sizes exist.