Classification Models for Assessing the Severity of Marine Accidents Based on Machine Learning

• Published in: International Journal of Safety and Security Engineering
• Main Author: Passarella R.; Safitri A.I.; Husni N.L.; Widyastuti R.; Veny H.
• Format: Article
• Language: English
• Published: International Information and Engineering Technology Association 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203051830&doi=10.18280%2fijsse.140418&partnerID=40&md5=4b1bb835bcf2cfe49f437c355af5bd21

Marine transport is still famous and claimed to be part of human civilization, but in practice, marine vessels still experience accidents quite frequently, which can result in large losses. Therefore, this research aims to integrate multiple data sources on marine accidents, classify them to identify patterns, and create a model to forecast and prevent future accidents. The first step in the methodology is to connect several variables from multiple data sources and generate target variables. We then feed this ready data set into 10 machine learning algorithms to determine which one best suit the data type and quality. The training results provided four algorithms with the best performance, namely label spreading, label propagation, random forest, and XGB classifier algorithms. After comparing the training and testing results, we found that XGB performed slightly better than the other three models, where the developed model and dataset only had a performance of 70%-74% in predicting marine accidents in the corresponding class. Copyright: © 2024 The authors.