Comparison of Machine Learning Algorithms for Discharge Prediction of Multipurpose Dam

Water

Jiyeong Hong¹  Kisung Kim²  Gwanjae Lee²  Kyoung Jae Lim²  Seoro Lee²  Jonggun Kim²  Dongseok Yang²  Joo Hyun Bae³ 

[1] Department of Earth and Environment, Boston University, Boston, MA 02215, USA;Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Korea;Korea Water Environment Research Institute, Chuncheon-si 24408, Korea;

关键词: dam discharge;    decision tree;    multilayer perceptron;    K-nearest neighbor;    support vector machine;    random forest;   

DOI  :  10.3390/w13233369

来源: DOAJ

【 摘 要 】

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 (NSE) of 0.796, root-mean-squared error (RMSE) of 48.996 m³/s, mean absolute error (MAE) of 10.024 m³/s, R of 0.898, and R² 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.

【 授权许可】

Unknown