Deep Learning Based Forecasting of Delay on Flights

Ayaydın A., Akcayol M. A.

INTERNATIONAL JOURNAL OF INFORMATICS TECHNOLOGIES, cilt.15, sa.3, ss.239-249, 2022 (Hakemli Dergi) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 3
  • Basım Tarihi: 2022
  • Doi Numarası: 10.17671/gazibtd.1060646
  • Dergi Adı: INTERNATIONAL JOURNAL OF INFORMATICS TECHNOLOGIES
  • Derginin Tarandığı İndeksler: Applied Science & Technology Source, Computer & Applied Sciences, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.239-249
  • Gazi Üniversitesi Adresli: Evet

Özet

In this study, three different methods from machine learning and deep learning have been implemented for preventing financial and moral losses that may occur as a result of delays in flights and to take necessary precautions by predicting the flight delay in advance, which are a serious problem in the aviation industry. Deep recurrent neural network (DRNN), long-short term memory (LSTM), and random forest (RF) have been extensively tested and compared employing a real data set covering 368 airports across the world with relevancy the success rate of forecasting of delay on flights. The experimental results showed that the LSTM model had a higher success rate of 96.50% at the recall level than the others.