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http://dx.doi.org/10.7837/kosomes.2018.24.4.408

An Analysis of Causes of Marine Incidents at sea Using Big Data Technique  

Kang, Suk-Young (Examination Administration Team, Korea Institute of Maritime and Fisheries Technology)
Kim, Ki-Sun (Marine Safety Team, Korea Institute of Maritime and Fisheries Technology)
Kim, Hong-Beom (Ocean Polytech Team, Korea Institute of Maritime and Fisheries Technology)
Rho, Beom-Seok (Education & Operation Team, Korea Institute of Maritime and Fisheries Technology)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.24, no.4, 2018 , pp. 408-414 More about this Journal
Abstract
Various studies have been conducted to reduce marine accidents. However, research on marine incidents is only marginal. There are many reports of marine incidents, but the main content of existing studies has been qualitative, which makes quantitative analysis difficult. However, quantitative analysis of marine accidents is necessary to reduce marine incidents. The purpose of this paper is to analyze marine incident data quantitatively by applying big data techniques to predict marine incident trends and reduce marine accident. To accomplish this, about 10,000 marine incident reports were prepared in a unified format through pre-processing. Using this preprocessed data, we first derived major keywords for the Marine incidents at sea using text mining techniques. Secondly, time series and cluster analysis were applied to major keywords. Trends for possible marine incidents were predicted. The results confirmed that it is possible to use quantified data and statistical analysis to address this topic. Also, we have confirmed that it is possible to provide information on preventive measures by grasping objective tendencies for marine incidents that may occur in the future through big data techniques.
Keywords
Marine incident; Quantitative analysis; Big data; Text mining; Time series & Cluster analysis;
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