| Summary: | For effective water management in the downstream area of a dam, it is necessary to estimate the amount of discharge from the dam to quantify the flow downstream of the dam. In this study, a machine learning model was constructed to predict the amount of discharge from Soyang River Dam using precipitation and dam inflow/discharge data from 1980 to 2020. Decision tree, multilayer perceptron, random forest, gradient boosting, RNN-LSTM, and CNN-LSTM were used as algorithms. The RNN-LSTM model achieved a Nash–Sutcliffe efficiency (<i>NSE</i>) of 0.796, root-mean-squared error (<i>RMSE</i>) of 48.996 m<sup>3</sup>/s, mean absolute error (<i>MAE</i>) of 10.024 m<sup>3</sup>/s, <i>R</i> of 0.898, and <i>R</i><sup>2</sup> of 0.807, showing the best results in dam discharge prediction. The prediction of dam discharge using machine learning algorithms showed that it is possible to predict the amount of discharge, addressing limitations of physical models, such as the difficulty in applying human activity schedules and the need for various input data.
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