Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow. Therefore, two combined machine learning (CombML) models (RF_MLP and GB_MLP) were developed for the prediction of the dam inflow using the ensemble methods (RF and GB) at precipitation below 16 mm, and the MLP at precipitation above 16 mm. The precipitation of 16 mm is the average daily precipitation at the inflow of 100 m3/s or more. The results show the accuracy verification results of NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, and R2 0.859 in RF_MLP, and NSE 0.829, RMSE 73.918 m3/s, MAE 18.093 m3/s, R 0.912, and R2 0.831 in GB_MLP, which infers that the combination of the models predicts the dam inflow the most accurately. CombML algorithms showed that it is possible to predict inflow through inflow learning, considering flow characteristics such as flow regimes, by combining several machine learning algorithms.

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用于预测大坝进水量的组合机器学习模型的开发和评估

预测大坝流入量对于有效的水资源管理是必要的。这项研究创建了机器学习算法，使用 40 年来的天气和大坝流入数据来预测韩国 Soyang 河大坝的流入量。一共使用了六种算法，如下：决策树（DT）、多层感知器（MLP）、随机森林（RF）、梯度提升（GB）、循环神经网络-长短期记忆（RNN-LSTM）、和卷积神经网络-LSTM（CNN-LSTM）。在这些模型中，多层感知器模型在预测大坝流入量方面的效果最好，纳什-萨特克利夫效率 (NSE) 值为 0.812，均方根误差 (RMSE) 为 77.218 m3/s，平均绝对误差 (MAE) 为29.034 m3/s，相关系数 (R) 为 0.924，决定系数 (R2) 为 0.817。然而，当大坝流入量低于 100 m3/s 时，集成模型（随机森林和梯度提升模型）在预测大坝流入量方面的表现优于 MLP。因此，开发了两种组合机器学习 (CombML) 模型（RF_MLP 和 GB_MLP），用于在降水量低于 16 mm 时使用集合方法（RF 和 GB）和在降水量高于 16 mm 时使用 MLP 预测大坝入流。16 毫米的降水量是流入量在 100 立方米/秒以上时的平均日降水量。结果显示RF_MLP中NSE 0.857, RMSE 68.417 m3/s, MAE 18.063 m3/s, R 0.927, R2 0.859, 以及NSE 0.829, RMSE 73.918 m3/s, MAE 312/s, R2 0.859的精度验证结果。 ，GB_MLP 中的 R2 为 0.831，由此推断模型组合对大坝入流的预测最为准确。