dc.contributor.author | Stefanovič, Pavel | |
dc.contributor.author | Štrimaitis, Rokas | |
dc.contributor.author | Kurasova, Olga | |
dc.date.accessioned | 2023-09-18T20:33:59Z | |
dc.date.available | 2023-09-18T20:33:59Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 1687-5265 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/150861 | |
dc.description.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. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-10 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | DOAJ | |
dc.relation.isreferencedby | EI Compendex Plus | |
dc.relation.isreferencedby | ProQuest Central | |
dc.relation.isreferencedby | PubMed | |
dc.relation.isreferencedby | J-Gate Portal | |
dc.relation.isreferencedby | Academic OneFile | |
dc.rights | Laisvai prieinamas internete | |
dc.source.uri | https://www.hindawi.com/journals/cin/2020/8878681/ | |
dc.source.uri | http://downloads.hindawi.com/journals/cin/2020/8878681.pdf | |
dc.source.uri | https://talpykla.elaba.lt/elaba-fedora/objects/elaba:73233382/datastreams/MAIN/content | |
dc.title | Prediction of flight time deviation for Lithuanian airports using supervised machine learning model | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.license | Creative Commons – Attribution – 4.0 International | |
dcterms.references | 24 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.institution | Vilniaus universitetas | |
dc.contributor.faculty | Fundamentinių mokslų fakultetas / Faculty of Fundamental Sciences | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.vgtuprioritizedfields | IK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems | |
dc.subject.ltspecializations | L106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies | |
dc.subject.en | supervised machine learning | |
dc.subject.en | classification | |
dc.subject.en | prediction | |
dc.subject.en | grid search | |
dc.subject.en | flight time deviation | |
dcterms.sourcetitle | Computational intelligence and neuroscience | |
dc.description.volume | vol. 2020 | |
dc.publisher.name | Hindawi | |
dc.publisher.city | London | |
dc.identifier.doi | 000590888500001 | |
dc.identifier.doi | 10.1155/2020/8878681 | |
dc.identifier.elaba | 73233382 | |