Prediction model for civil aircraft hard landing Informer neural network

The prediction of hard landing is of great significance to support the pilot's landing decision and ensure flight safety. Aiming at the short prediction time and poor explainability of the existing hard landing prediction models, the Informer hard landing prediction model based on QAR data is proposed, and the explainability analysis for the output of the model prediction is conducted. Firstly, the QAR data are processed by using a series of appropriate methods such as forward-backward filtering and Granger causality test to establish a dataset that meets the prediction demand. Secondly, according to the characteristics of multivariate time series discontinuity, the Informer network is localized to extend the range of accepted data. Then, the hyper parameters of the model are optimized in training and testing to ensure that the model has high prediction accuracy and good generalization. Finally, the output attention weight matrix improves the transparency of the model decision. Experiments show that the localization-improved Informer neural network improves the prediction accuracy by 23.5% and enhances the learning ability of discontinuous multivariate time series; compared with the LSTM network, the prediction accuracy improves by 18.83% overall, and it has a better ability to predict long time series.