Sensitive Resource and Traffic Density Risk Analysis of Marine Spill Accidents Using Automated Identification System Big Data

Abstract

This study developed a new methodology for analyzing the risk level of marine spill accidents from two perspectives, namely, marine traffic density and sensitive resources. Through a case study conducted in Busan, South Korea, detailed procedures of the methodology were proposed and its scalability was confirmed. To analyze the risk from a more detailed and microscopic viewpoint, vessel routes as hazard sources were delineated on the basis of automated identification system (AIS) big data. The outliers and errors of AIS big data were removed using the density-based spatial clustering of applications with noise algorithm, and a marine traffic density map was evaluated by combining all of the gridded routes. Vulnerability of marine environment was identified on the basis of the sensitive resource map constructed by the Korea Coast Guard in a similar manner to the National Oceanic and Atmospheric Administration environmental sensitivity index approach. In this study, aquaculture sites, water intake facilities of power plants, and beach/resort areas were selected as representative indicators for each category. The vulnerability values of neighboring cells decreased according to the Euclidean distance from the resource cells. Two resulting maps were aggregated to construct a final sensitive resource and traffic density (SRTD) risk analysis map of the Busan–Ulsan sea areas. We confirmed the effectiveness of SRTD risk analysis by comparing it with the actual marine spill accident records. Results show that all of the marine spill accidents in 2018 occurred within 2 km of high-risk cells (level 6 and above). Thus, if accident management and monitoring capabilities are concentrated on high-risk cells, which account for only 6.45% of the total study area, then it is expected that it will be possible to cope with most marine spill accidents effectively.

Keywords

SRTD risk analysis / AIS big data / Sensitive resource / Marine spill accidents / Marine traffic / Traffic density / Marine oil spill

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