Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model
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 perc...
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Series: | Computational Intelligence and Neuroscience |
Online Access: | http://dx.doi.org/10.1155/2020/8878681 |
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doaj-aa2b3e0f9ab14eb4b9a8955d3da240262020-11-25T04:10:02ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52732020-01-01202010.1155/2020/88786818878681Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning ModelPavel Stefanovič0Rokas Štrimaitis1Olga Kurasova2Faculty of Fundamental ScienceFaculty of Fundamental ScienceInstitute of Data Science and Digital TechnologiesIn 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.http://dx.doi.org/10.1155/2020/8878681 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pavel Stefanovič Rokas Štrimaitis Olga Kurasova |
spellingShingle |
Pavel Stefanovič Rokas Štrimaitis Olga Kurasova Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model Computational Intelligence and Neuroscience |
author_facet |
Pavel Stefanovič Rokas Štrimaitis Olga Kurasova |
author_sort |
Pavel Stefanovič |
title |
Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model |
title_short |
Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model |
title_full |
Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model |
title_fullStr |
Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model |
title_full_unstemmed |
Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model |
title_sort |
prediction of flight time deviation for lithuanian airports using supervised machine learning model |
publisher |
Hindawi Limited |
series |
Computational Intelligence and Neuroscience |
issn |
1687-5273 |
publishDate |
2020-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2020/8878681 |
work_keys_str_mv |
AT pavelstefanovic predictionofflighttimedeviationforlithuanianairportsusingsupervisedmachinelearningmodel AT rokasstrimaitis predictionofflighttimedeviationforlithuanianairportsusingsupervisedmachinelearningmodel AT olgakurasova predictionofflighttimedeviationforlithuanianairportsusingsupervisedmachinelearningmodel |
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