Assessment of Activating Reservoir Emergency Storage in Climate-Change-Fueled Extreme Drought

Chae, Heechan; Ji, Jungwon; Lee, Eunkyung; Lee, Seonmi; Choi, Youngje; Yi, Sooyeon; Yi, Jaeeung

doi:10.3390/w14203242

Cited by 6 publications

(1 citation statement)

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“…South Korea experiences four distinct seasons, each with its unique climate characteristics. Winter is cold with occasional snowfall, while summer is hot and humid, accompanied by a rainy season that typically spans from June to August (Chae et al., 2022). During the summer months (June to August), average rainfall amounts to 634 millimeters (mm).…”

Section: Study Areamentioning

confidence: 99%

Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea

Yi,

Kondolf,

Sandoval Solis

et al. 2024

Water Resources Research

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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.

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Section: Study Areamentioning

confidence: 99%