An Improved Artificial Neural Network Model for Flights Delay Prediction

Abstract

With the limitation of air traffic and the rapid increase in the number of flights, flight delay is becoming more frequent. Flight delay leads to financial and time losses for passengers and increases operating costs for airlines. Therefore, the establishment of an accurate prediction model for flight delay becomes vital to build an efficient airline transportation system. The air transportation system has a huge amount of data and complex operation modes, which is suitable for analysis by using machine learning methods. This paper discusses the factors that may affect the flight delay, and presents a new flight delay prediction model. The five warning levels are defined based on flight delay database by using K-means clustering algorithm. After extracting the key factors related to flight operation by the grey relational analysis (GRA) algorithm, an improved machine learning algorithm called GRA — Genetic algorithm (GA) — back propagation neural network, GRA-GA-BP, is introduced, which is optimized by GA. The calculation results show that, compared with models before optimization and other two algorithms in previous papers, the proposed prediction model based on GRA-GA-BP algorithm shows a higher prediction accuracy and more stability. In terms of operation efficiency and memory consumption, it also has good performance. The analysis presented in this paper indicates that this model can provide effective early warnings for flight delay, and can help airlines to intervene in flights with abnormal trend in advance.

Research on forecasting model of corporate financial crisis based on pattern recognition and artificial intelligence

“…Tanik et al [21] studied the company's financial crisis prediction model by combining genetic algorithm and moderate financial indicators, and the results showed that the prediction model had high accuracy compared with other prediction models. Nagendran et al [22] studied a set of financial crisis warning models by adopting the method of ensemble learning and compared it with the neural network algorithm. The research results showed that the accuracy of the algorithm model was higher than that of the neural network algorithm.…”