Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model
Joint Authors
Stefanovič, Pavel
Štrimaitis, Rokas
Kurasova, Olga
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-27
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
In the paper, the flight time deviation of Lithuania airports has been analyzed.
The supervised machine learning model has been implemented to predict the interval of time delay deviation of new flights.
The analysis has been made using seven algorithms: probabilistic neural network, multilayer perceptron, decision trees, random forest, tree ensemble, gradient boosted trees, and support vector machines.
To find the best parameters which give the highest accuracy for each algorithm, the grid search has been used.
To evaluate the quality of each algorithm, the five measures have been calculated: sensitivity/recall, precision, specificity, F-measure, and accuracy.
All experimental investigation has been made using the newly collected dataset from Lithuania airports and weather information on departure/landing time.
The departure flights and arrival flights have been investigated separately.
To balance the dataset, the SMOTE technique is used.
The research results showed that the highest accuracy is obtained using the tree model classifiers and the best algorithm of this type to predict is gradient boosted trees.
American Psychological Association (APA)
Stefanovič, Pavel& Štrimaitis, Rokas& Kurasova, Olga. 2020. Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1138941
Modern Language Association (MLA)
Stefanovič, Pavel…[et al.]. Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1138941
American Medical Association (AMA)
Stefanovič, Pavel& Štrimaitis, Rokas& Kurasova, Olga. Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1138941
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138941