PROGNOSIS METHOD OF UNFAVORABLE AIRBORNE EVENTS DURING FLIGHT BASED ON CONVOLUTIONAL AND RECURRENT NEURAL NETWORKS

Abstract

This paper contains formal problem definition of predicting unfavorable airborne events during flight. Restrictions and assumptions are put into the prognosis method of unfavorable airborne events during flight. Mathematical apparatus used to build prognosis method is suggested. As a basic mathematical apparatus it is suggested to use, recurrent neural networks (RNN) based on LSTM modules and convolutional neural networks (CNN). Analysis of these neural networks has shown that RNN based on LSTM modules are mostly effective when analyzing structured text, such as report of investigation of airborne accidents. In its turn, CNN are effective when analyzing unstructured text, such as text messages about the flight situation based on the information from external sources. Prognosis method of unfavorable airborne events during flight based on convolutional and recurrent neural networks is developed. In case of solving the task of prediction of unfavorable airborne events during flight RNN are used for initial setup of the Embedding layer of the structured training data in the process of hybrid neural network training. CNN are used during the direct operation of hybrid neural network model of prediction of unfavorable airborne events during flight.