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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使用机器学习进行地下水位预测：韩国四大河流项目百济堰案例研究

了解人造结构对地下水位的影响至关重要，水坝或堰等结构给研究带来了独特的挑战和机会。韩国的百济堰是一个有趣的案例，因为堰已经完全打开，而堰和水库通常不会出现这种情况，这为模拟堰去除条件提供了宝贵的机会。主要目标是调查各种堰作业下的地下水位波动、距堰的距离以及季节变化。该研究利用观测数据模拟有和没有堰的条件，包括闸门全开的情况。多种机器学习算法——随机森林 (RF)、人工神经网络、支持向量回归 (SVR)、梯度提升和极限梯度提升 (XGBoost)——用于开发准确的地下水位预测模型。使用确定系数、均方根误差 (RMSE)、平均绝对误差 (MAE) 指数评估模型的性能，并通过泰勒图进行可视化。结果表明，XGBoost 在训练和测试阶段均优于所有三组中的其他模型。具体来说，XGBoost 在训练中优于 RF 2.09% ( R ² )、5.66% (RMSE) 和 10.1% (MAE)，并且优于 SVR 11.2% ( R ² )、42.0% (RMSE) 和 129.2% (MAE) ）在测试中。此外，该研究还生成了地下水位图，为管理地下水系统和为堰运营决策提供了实用工具。这项研究不仅揭示了堰作业与地下水位之间的动态关系，而且还为类似水文环境中的有效水管理提供了可行的见解。