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Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion Risks

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  • Jeongho Han

    (Agriculture and Life Sciences Research Institute, Kangwon National University, Chuncheon-si 24341, Republic of Korea)

  • Seoro Lee

    (Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Republic of Korea)

Abstract

Soil erosion due to rainfall is a critical environmental issue in North Korea, exacerbated by deforestation and climate change. This study aims to estimate rainfall erosivity (RE) in North Korea using automated machine learning (AutoML), with a particular focus on regional soil erosion risks. North Korean data were sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis 5 dataset, while South Korean data were obtained from the Korea Meteorological Administration. Data from 50 stations in South Korea (2013–2019) and 27 stations in North Korea (1980–2020) were used. The GradientBoostingRegressor (GBR) model, optimized using the Tree-based Pipeline Optimization Tool (TPOT), was trained on South Korean data. The model’s performance was evaluated using metrics such as the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2 ), achieving high predictive accuracy across eight stations in South Korea. Using the optimized model, RE in North Korea was estimated, and the spatial distribution of RE was analyzed using the Kriging interpolation. Results reveal significant regional variability, with the southern and western areas displaying the highest erosivity. These findings provide valuable insights into soil erosion management and the development of sustainable agricultural and environmental strategies in North Korea.

Suggested Citation

  • Jeongho Han & Seoro Lee, 2024. "Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion Risks," Land, MDPI, vol. 13(12), pages 1-14, November.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:12:p:2038-:d:1531893
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    1. Chen, Ji-Long & Liu, Hong-Bin & Wu, Wei & Xie, De-Ti, 2011. "Estimation of monthly solar radiation from measured temperatures using support vector machines – A case study," Renewable Energy, Elsevier, vol. 36(1), pages 413-420.
    2. Kieu Anh Nguyen & Walter Chen & Bor-Shiun Lin & Uma Seeboonruang, 2020. "Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
    3. Abdulaziz Alqahtani & Muhammad Izhar Shah & Ali Aldrees & Muhammad Faisal Javed, 2022. "Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality," Sustainability, MDPI, vol. 14(3), pages 1-19, January.
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