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Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model.
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- Author(s): Stefanovič, Pavel1 (AUTHOR); Štrimaitis, Rokas1 (AUTHOR); Kurasova, Olga2 (AUTHOR)
- Source:
Computational Intelligence & Neuroscience. 10/27/2020, p1-10. 10p.- Subject Terms:
- Source:
- Additional Information
- Subject Terms:
- Abstract: 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. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Computational Intelligence & Neuroscience is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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