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

Jeongho Han and Seoro Lee ()
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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

Land, 2024, vol. 13, issue 12, 1-14

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.

Keywords: rainfall erosivity; RUSLE; machine learning; soil erosion; North Korea; automated machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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