Analysis and forecasting of national marine litter based on coastal data in South Korea from 2009 to 2021
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Updated: Aug 7, 2025
Data Collection on Marine Litter Ingestion in Sea Turtles and Thresholds for Good Environmental Status
Published on: May 18, 2019
Min-Ho Park1, Siljung Yeo2, Seung-Kwon Yang3
1Division of Marine Engineering, Korea Maritime and Ocean University, Busan 49112, Republic of Korea; Interdisciplinary Major of Maritime and AI Convergence, Korea Maritime and Ocean University, Busan 49112, Republic of Korea.
Abstract:
In this study, statistical analysis and forecasting were performed using coastal litter data of Korea. The analysis indicated that rope and vinyl accounted for the highest proportion of coastal litter items. The statistical analysis of the national coastal litter trends revealed that the greatest concentration of litter was observed during summer months (June-August). To predict the amount of coastal litter per meter, recurrent neural network (RNN)-based models were used. Neural basis expansion analysis for interpretable time series forecasting (N-BEATS) and neural hierarchical interpolation for time series forecasting (N-HiTS), an improved model of N-BEATS recently announced, were used for comparison with RNN-based models. When predictive performance and trend followability were evaluated, overall N-BEATS and N-HiTS outperformed RNN-based models. Furthermore, we found that average of N-BEATS and N-HiTS models yielded better results than using one model.
