Estimating the Amount of Submerged Marine Debris Based on Fishing Vessels Using Multiple Regression Model

Kyounghwan Song, Seunghyun Lee (), Taehwan Joung, Jiwon Yu and Jongkoo Park
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Kyounghwan Song: Air Solution R&D Lab, H&A Company, LG Electronics, Changwon 51554, Republic of Korea
Seunghyun Lee: Maritime Digital Transformation Research Centre, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Republic of Korea
Taehwan Joung: International Maritime Research Centre, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Republic of Korea
Jiwon Yu: Maritime Digital Transformation Research Centre, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Republic of Korea
Jongkoo Park: A.I. Platform Department, HancomInSpace Co., Ltd., Daejeon 34103, Republic of Korea

Sustainability, 2023, vol. 15, issue 20, 1-10

Abstract: The majority of marine debris is found in shallow waters; however, submerged debris accumulated at the sea bottom is affected by this kind of pollution. To mitigate the harmful effect of marine debris, we have to recognize its characteristics. However, it is hard to estimate the quantity of submerged marine debris because the monitoring of submerged marine debris requires greater cost and time compared to the monitoring of beach or coastal debris. In this study, we used the data for submerged marine debris surveyed in the sea near the Korean Peninsula from 2017 to 2020 and the data of fishing vessels passing through the areas from 2018 to 2020. In addition, the correlation of major factors affecting the amount of submerged marine debris was analyzed based on the fishing vessel data and the removal project data for submerged marine debris. Moreover, we estimated the amount of submerged marine debris based on the fishing vessels at the collection sites surveyed two or more times using a stepwise regression model. The average amount of submerged marine debris estimated by the model was 6.0 tonnes more than that by the removal project, for which the error was ~26.5% compared to the amount collected by the removal project. The estimation method for submerged marine debris developed in this study can provide crucial information for an effective collection project by suggesting areas that require a collection project for submerged marine debris based on the information of fishing vessels.

Keywords: fishing vessels; marine debris; sink debris; regression analysis; submerged marine debris (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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