Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea
Abstract Understanding the impact of human‐made structures on groundwater levels is essential, with structures like dams or weirs presenting unique challenges and opportunities for study. The Baekje weir in South Korea presents an interesting case as the weir has undergone full gate opening, which i...
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| Format: | Article |
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Wiley
2024-05-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2022WR032779 |
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| author | Sooyeon Yi G. Mathias Kondolf Samuel Sandoval Solis Larry Dale |
| author_facet | Sooyeon Yi G. Mathias Kondolf Samuel Sandoval Solis Larry Dale |
| author_sort | Sooyeon Yi |
| collection | DOAJ |
| description | Abstract Understanding the impact of human‐made structures on groundwater levels is essential, with structures like dams or weirs presenting unique challenges and opportunities for study. The Baekje weir in South Korea presents an interesting case as the weir has undergone full gate opening, which is generally not the case for weirs and reservoirs, providing valuable opportunity for simulating weir removal conditions. The main objectives are investigation of groundwater level fluctuations under various weir operations, distances from the weir, and seasonal variations. The study utilizes observed data that simulates conditions with and without the weir, including scenarios of full gate opening. Multiple machine learning algorithms—Random Forest (RF), Artificial Neural Network, Support Vector Regression (SVR), Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—are used to develop accurate groundwater level prediction models. The models' performance is assessed using coefficient of determination, Root mean square error (RMSE), Mean Absolute Error (MAE) indices, and visualized through Taylor diagrams. Results indicate that XGBoost outperforms other models in all three groups during both training and testing phases. Specifically, XGBoost surpasses RF by 2.09% (R2), 5.66% (RMSE), and 10.1% (MAE) in training, and outperforms SVR by 11.2% (R2), 42.0% (RMSE), and 129.2% (MAE) in testing. Additionally, the study generates groundwater level maps, providing a practical tool for managing groundwater systems and informing decision‐making in weir operations. This study not only sheds light on the dynamic relationship between weir operations and groundwater levels but also provides actionable insights for effective water management in similar hydrological settings. |
| format | Article |
| id | doaj-art-1254fbb44d6d4a15ae4518b231a2b48e |
| institution | OA Journals |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Wiley |
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| series | Water Resources Research |
| spelling | doaj-art-1254fbb44d6d4a15ae4518b231a2b48e2025-08-20T02:09:32ZengWileyWater Resources Research0043-13971944-79732024-05-01605n/an/a10.1029/2022WR032779Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South KoreaSooyeon Yi0G. Mathias Kondolf1Samuel Sandoval Solis2Larry Dale3Department of Environmental Science, Policy, and Management University of California, Berkeley Berkeley CA USALandscape Architecture and Environmental Planning University of California, Berkeley Berkeley CA USADepartment of Land, Air, and Water Resources University of California, Davis Davis CA USAEnergy and Resources Group University of California, Berkeley Berkeley CA USAAbstract Understanding the impact of human‐made structures on groundwater levels is essential, with structures like dams or weirs presenting unique challenges and opportunities for study. The Baekje weir in South Korea presents an interesting case as the weir has undergone full gate opening, which is generally not the case for weirs and reservoirs, providing valuable opportunity for simulating weir removal conditions. The main objectives are investigation of groundwater level fluctuations under various weir operations, distances from the weir, and seasonal variations. The study utilizes observed data that simulates conditions with and without the weir, including scenarios of full gate opening. Multiple machine learning algorithms—Random Forest (RF), Artificial Neural Network, Support Vector Regression (SVR), Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—are used to develop accurate groundwater level prediction models. The models' performance is assessed using coefficient of determination, Root mean square error (RMSE), Mean Absolute Error (MAE) indices, and visualized through Taylor diagrams. Results indicate that XGBoost outperforms other models in all three groups during both training and testing phases. Specifically, XGBoost surpasses RF by 2.09% (R2), 5.66% (RMSE), and 10.1% (MAE) in training, and outperforms SVR by 11.2% (R2), 42.0% (RMSE), and 129.2% (MAE) in testing. Additionally, the study generates groundwater level maps, providing a practical tool for managing groundwater systems and informing decision‐making in weir operations. This study not only sheds light on the dynamic relationship between weir operations and groundwater levels but also provides actionable insights for effective water management in similar hydrological settings.https://doi.org/10.1029/2022WR032779groundwater level predictionmachine learningdam operationFour Major Rivers ProjectGeum River Basin |
| spellingShingle | Sooyeon Yi G. Mathias Kondolf Samuel Sandoval Solis Larry Dale Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea Water Resources Research groundwater level prediction machine learning dam operation Four Major Rivers Project Geum River Basin |
| title | Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea |
| title_full | Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea |
| title_fullStr | Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea |
| title_full_unstemmed | Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea |
| title_short | Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea |
| title_sort | groundwater level forecasting using machine learning a case study of the baekje weir in four major rivers project south korea |
| topic | groundwater level prediction machine learning dam operation Four Major Rivers Project Geum River Basin |
| url | https://doi.org/10.1029/2022WR032779 |
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