Predicting the risky encounters without distance knowledge between the ships via machine learning algorithms
dc.contributor.author | Oruç, Muhammet Furkan | |
dc.contributor.author | Altan, Yiğit Can | |
dc.date.accessioned | 2023-08-17T09:07:03Z | |
dc.date.available | 2023-08-17T09:07:03Z | |
dc.date.issued | 2023-07-01 | |
dc.identifier.issn | 0957-4174 | en_US |
dc.identifier.uri | http://hdl.handle.net/10679/8709 | |
dc.identifier.uri | https://www.sciencedirect.com/science/article/abs/pii/S0957417423002294 | |
dc.description.abstract | As the maritime traffic is getting denser, the number of encounters is increasing. The aim of this study is to develop a prediction model to classify encounters as risky or non-risky when two ships encounter in a certain buffer zone. A novel methodology is proposed to integrate three-dimensional clustering in the algorithm training process. K-means clustering, and ensemble machine learning algorithms-based prediction framework is developed to overcome class imbalance. The methodology is tested in the Strait of Istanbul (SOI) and parameters are generated from a long-term AIS dataset. Framework is validated via cross validation techniques. Precision, Recall, Accuracy and ROC-AUC Score are used as measures to evaluate models. Benchmark models are generated, and the most advanced model successfully predicts each 4 out of 5 risky encounters without the knowledge of distance between two ships. Eliminating distance from decision factors provides an action period before risky encounters. Therefore, proposed framework can be a guide for autonomous vessels for safe navigation and maritime authorities to improve maritime safety. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartof | Expert Systems with Applications | |
dc.rights | restrictedAccess | |
dc.title | Predicting the risky encounters without distance knowledge between the ships via machine learning algorithms | en_US |
dc.type | Article | en_US |
dc.peerreviewed | yes | en_US |
dc.publicationstatus | Published | en_US |
dc.contributor.department | Özyeğin University | |
dc.contributor.authorID | (ORCID 0000-0003-0208-6867 & YÖK ID 163795) Altan, Yiğit | |
dc.contributor.ozuauthor | Altan, Yiğit Can | |
dc.identifier.volume | 221 | en_US |
dc.identifier.wos | WOS:000953317300001 | |
dc.identifier.doi | 10.1016/j.eswa.2023.119728 | en_US |
dc.subject.keywords | Autonomous Vessels | en_US |
dc.subject.keywords | Clustering | en_US |
dc.subject.keywords | Machine Learning | en_US |
dc.subject.keywords | Maritime Risk | en_US |
dc.subject.keywords | Risky Encounter | en_US |
dc.subject.keywords | Strait of Istanbul | en_US |
dc.identifier.scopus | SCOPUS:2-s2.0-85150765105 | |
dc.contributor.ozugradstudent | Oruç, Muhammet Furkan | |
dc.relation.publicationcategory | Article - International Refereed Journal - Institutional Academic Staff and Graduate Student |
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