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http://dx.doi.org/10.7837/kosomes.2022.28.5.780

Analysis and Prediction Methods of Marine Accident Patterns related to Vessel Traffic using Long Short-Term Memory Networks  

Jang, Da-Un (Graduate School of Maritime Transportation System, Mokpo National Maritime University)
Kim, Joo-Sung (Division of Navigation Science, Mokpo National Maritime University)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.28, no.5, 2022 , pp. 780-790 More about this Journal
Abstract
Quantitative risk levels must be presented by analyzing the causes and consequences of accidents and predicting the occurrence patterns of the accidents. For the analysis of marine accidents related to vessel traffic, research on the traffic such as collision risk analysis and navigational path finding has been mainly conducted. The analysis of the occurrence pattern of marine accidents has been presented according to the traditional statistical analysis. This study intends to present a marine accident prediction model using the statistics on marine accidents related to vessel traffic. Statistical data from 1998 to 2021, which can be accumulated by month and hourly data among the Korean domestic marine accidents, were converted into structured time series data. The predictive model was built using a long short-term memory network, which is a representative artificial intelligence model. As a result of verifying the performance of the proposed model through the validation data, the RMSEs were noted to be 52.5471 and 126.5893 in the initial neural network model, and as a result of the updated model with observed datasets, the RMSEs were improved to 31.3680 and 36.3967, respectively. Based on the proposed model, the occurrence pattern of marine accidents could be predicted by learning the features of various marine accidents. In further research, a quantitative presentation of the risk of marine accidents and the development of region-based hazard maps are required.
Keywords
Vessel traffic; Long short-term memory network; Recurrent neural network; Marine accident; Pattern recognition;
Citations & Related Records
Times Cited By KSCI : 9  (Citation Analysis)
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