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Creative Commons licence CC BY-NC (Attribution-NonCommercial)Logforum. 2022. 18(1), article 8, 97-107; DOI: https://doi.org/10.17270/J.LOG.2022.655


Irmak Hatıpoğlu1, Ömür Tosun2, Nedret Tosun3

1Department of International Trade and Logistics, Faculty of Applied Sciences, Antalya, Turkey
Department of Management Information Systems, Faculty of Applied Sciences, Akdeniz University, Antalya, Turkey 3West Mediterranean Exportaters Association, Antalya, Turkey


Background: The delay of a planned flight causes many undesirable situations such as cost, customer satisfaction, environmental pollution. There is only one way to prevent these problems before they occur, and that is to know which flights will be delayed. The aim of this study is to predict delayed flights. For this, the use of machine learning techniques, which have become widespread with the development of computer capacities and data storage systems, is preferred.

Methods: Estimations are made with three up-to-date techniques XGBoost, LightGBM, and CatBoost techniques based on Gradient Boosting from machine learning techniques. The bayesian technique is used for hyper-parameter settings. In addition, the Synthetic Minority Over-Sampling Technique (SMOTE) technique is also used, as the majority of flights are on time and delayed flights, which constitute a minority class, may adversely affect the results. The results are analyzed and shared with and without SMOTE.

Results: As a consequence of the application, which was run on a data set containing all of an international airline's flights [18148 flights] for a year, it was discovered that flights may be predicted with high accuracy.

Conclusions: The application of machine learning techniques to anticipate flight delays is new, but it has a lot of potential. Companies will be able to avert problems before they develop if delays are correctly estimated, which can generate plenty of issues. As a result, concrete advantages such as lower costs and higher customer satisfaction will emerge. Improvements will be made at the most vulnerable place in the aviation business.

Keywords: GBDT, XGBoost, LightGBM, Catboost, delay prediction
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MLA Hatıpoğlu, Irmak, et al. "Flight delay prediction based with machine learning." Logforum 18.1 (2022): 8. DOI: https://doi.org/10.17270/J.LOG.2022.655
APA Irmak Hatıpoğlu, Ömür Tosun, Nedret Tosun (2022). Flight delay prediction based with machine learning. Logforum 18 (1), 8. DOI: https://doi.org/10.17270/J.LOG.2022.655
ISO 690 HATIPOĞLU, Irmak, TOSUN, Ömür, TOSUN, Nedret. Flight delay prediction based with machine learning. Logforum, 2022, 18.1: 8. DOI: https://doi.org/10.17270/J.LOG.2022.655