Predicting the risky encounters without distance knowledge between the ships via machine learning algorithms
Type :
Article
Publication Status :
Published
Access :
restrictedAccess
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.
Source :
Expert Systems with Applications
Date :
2023-07-01
Volume :
221
Publisher :
Elsevier
URI
http://hdl.handle.net/10679/8709https://www.sciencedirect.com/science/article/abs/pii/S0957417423002294
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