A multi-factor flight delay prediction and evaluation method based on artificial neural network and cloud model

The purpose of this paper is to combine the cloud model method with the Artificial neural network to predict and evaluate flight delay date. In this paper, the flight delay data of the United States in recent 15 years were collected, involving nine airlines and seven delay causes. Then, the flight delay prediction model is obtained by combining neural network and cloud model theory. 36 sets of data are extracted to verify the accuracy of the prediction model. Finally, the delay rate is predicted and the prediction data is evaluated by cloud model. The results show that the amount of data to be processed is reduced to 20 % after combining the cloud model with the neural network. In the model validation, the average expected value deviation of flight delay prediction model is 23.1 %. In the analysis and evaluation of the prediction results, the delay cause 6 is taken as an example, with the highest delay rate of 7.03 % in December. The delay risk assessment results for all months were general. The method provided in this paper can predict flight delays and provide theoretical support for the delay prevention of airlines.