TY  - JOUR
TI  - Deep Learning Based Forecasting of Delay on Flights
AB  - 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.
AU  - Ayaydın, Anıl
AU  - Akcayol, M. Ali
DO  - 10.17671/gazibtd.1060646
PY  - 2022
JO  - Bilişim Teknolojileri Dergisi
VL  - 15
IS  - 3
SN  - 1307-9697
SP  - 239
EP  - 249
DB  - TRDizin
UR  - http://search/yayin/detay/1130829
ER  -