Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion Risks
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...
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MDPI AG
2024-11-01
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| author | Jeongho Han Seoro Lee |
| author_facet | Jeongho Han Seoro Lee |
| author_sort | Jeongho Han |
| collection | DOAJ |
| description | 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<sup>2</sup>), 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. |
| format | Article |
| id | doaj-art-d75da99457644c3bb78894b81f2943c0 |
| institution | DOAJ |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Land |
| spelling | doaj-art-d75da99457644c3bb78894b81f2943c02025-08-20T02:57:19ZengMDPI AGLand2073-445X2024-11-011312203810.3390/land13122038Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion RisksJeongho Han0Seoro Lee1Agriculture and Life Sciences Research Institute, Kangwon National University, Chuncheon-si 24341, Republic of KoreaDepartment of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Republic of KoreaSoil 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<sup>2</sup>), 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.https://www.mdpi.com/2073-445X/13/12/2038rainfall erosivityRUSLEmachine learningsoil erosionNorth Koreaautomated machine learning |
| spellingShingle | Jeongho Han Seoro Lee Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion Risks Land rainfall erosivity RUSLE machine learning soil erosion North Korea automated machine learning |
| title | Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion Risks |
| title_full | Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion Risks |
| title_fullStr | Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion Risks |
| title_full_unstemmed | Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion Risks |
| title_short | Estimating Rainfall Erosivity in North Korea Using Automated Machine Learning: Insights into Regional Soil Erosion Risks |
| title_sort | estimating rainfall erosivity in north korea using automated machine learning insights into regional soil erosion risks |
| topic | rainfall erosivity RUSLE machine learning soil erosion North Korea automated machine learning |
| url | https://www.mdpi.com/2073-445X/13/12/2038 |
| work_keys_str_mv | AT jeonghohan estimatingrainfallerosivityinnorthkoreausingautomatedmachinelearninginsightsintoregionalsoilerosionrisks AT seorolee estimatingrainfallerosivityinnorthkoreausingautomatedmachinelearninginsightsintoregionalsoilerosionrisks |