Derin Öğrenme Tabanlı Havacılık Uçuş Verilerinde Gecikme Durumunun Tahmin Edilmesi

Yıl 2022, , 239 - 249, 31.07.2022

Anıl Ayaydın , M. Ali Akcayol

https://doi.org/10.17671/gazibtd.1060646

Cited By: 4

Öz

Bu çalışmada, havacılık endüstrisinde ciddi bir sorun teşkil eden uçuşlarda yaşanan gecikmeler sonucu oluşabilecek maddi-manevi kayıpları önlemek ve uçuş gecikmesinin önceden tahmin edilerek gerekli önlemlerin alınabilmesi amacıyla makine öğrenmesi ve derin öğrenmeden oluşan üç farklı yöntem uygulanmıştır. Deep recurrent neural networks (DRNN), long-short term memory (LSTM) ve random forest (RF) yöntemleri kapsamlı bir şekilde test edilmiş ve dünya genelinde 368 havalimanını kapsayan gerçek bir veri seti kullanılarak uçuşların gecikme durumu tahmin edilmiştir. Deneysel sonuçlar, LSTM modelinin %96.50 recall değeriyle diğer modellere göre daha yüksek başarı oranına sahip olduğunu göstermiştir.

Anahtar Kelimeler

tahminleme, derin öğrenme, makine öğrenmesi, havacılık

Deep Learning Based Forecasting of Delay on Flights

Öz

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.

Anahtar Kelimeler

estimation, deep learning, machine learning, aviation

Kaynakça

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