Machine Learning-Based Models for Accident Prediction at a Korean Container Port

Jae Hun Kim,Juneyoung Park,Gunwoo Lee,Juyeon Kim

doi:10.3390/su13169137

Abstract

The occurrence of accidents at container ports results in damages and economic losses in the terminal operation. Therefore, it is necessary to accurately predict accidents at container ports. Several machine learning models have been applied to predict accidents at a container port under various time intervals, and the optimal model was selected by comparing the results of different models in terms of their accuracy, precision, recall, and F1 score. The results show that a deep neural network model and gradient boosting model with an interval of 6 h exhibits the highest performance in terms of all the performance metrics. The applied methods can be used in the predicting of accidents at container ports in the future.

Highlights

Most of the existing studies on the risk assessment of maritime ports have focused on port security [1,2,3] and port safety [4,5,6]
Several researchers have implemented risk assessment methods to identify the risk factors associated with container ports [4,5,6], research regarding the prediction of container port accidents remains limited
As described in Section 2.1.4, Synthetic Minority Oversampling Technique (SMOTE) was applied to the dataset for training to overcome an imbalanced data issue and enhance the model performances

Summary

Introduction

Most of the existing studies on the risk assessment of maritime ports have focused on port security [1,2,3] and port safety [4,5,6]. Most studies focusing on the security considered only unusual events such as hostile attacks [7] and the smuggling of weapons [8], and most studies pertaining to port safety focused on accidents that occurred during usual port activities such as loading, discharging, importing, and exporting. Several activities, including the loading, discharging, importing, and exporting of containers are performed by port workers using equipment such as yard tractors and container cranes Owing to these extensive activities, container ports are prone to accidents, such as equipment–equipment collisions, equipment–container collisions, injuries, and container damages during discharging, loading, and moving. These accidents may result in damages to workers, equipment, and containers, as well as in economic loss

Methods

Results

Discussion

Conclusion