نویسنده
Oruç, Muhammet Furkan, Altan, Yiğit Can
تاریخ انتشار
2023-07-01
محل انتشار
-
Elsevier
موضوع
Autonomous Vessels, Clustering, Machine Learning, Maritime Risk, Risky Encounter, Strait of Istanbul
نوع
دوره ای
زبان
انگلیسی
دیجیتال
بله
نسخه خطی
خیر
کتابخانه
دانشگاه اوزیغین
شناسه دارایی کتابخانه
0957-4174
شماره ثبت
ec2f2388-4a1f-4d74-9380-5a7868ab0d33
محل کتابخانه
Civil Engineering
تاریخ
2023-07-01
متن نمونه
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.
DOI
10.1016/j.eswa.2023.119728
Cilt
221
