Development of an Interpretable Maritime Accident Prediction System Using Machine Learning Techniques

Abstract

Every year, maritime accidents cause severe damages not only to humans but also to maritime instruments like vessels. The authors of this work therefore propose a machine learning-based maritime accident prediction system that can be used to prevent maritime accidents from happening by predicting and interpreting the accidents. This work overcomes the limitations of the existing works that lack practicability in the sense that the ex-post analyses are conducted to suggest accident prevention strategies but maritime accidents are not analyzed holistically. Using extensive literature reviews and expert interviews, a large number of risk factors associated with maritime accidents are identified, and related data are collected and utilized in the work. Throughout variable selection, data retrieval, hot-spot identification, and the maritime accident prediction model construction process, various machine learning algorithms are exploited in order to construct an organized system. In addition, interpretations for the resulting accident predictions are given using interpretable machine learning algorithms so as to provide explainable results to users. Finally, the proposed system is evaluated using a SERVQUAL model and proves its effectiveness in real-world applications.