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1. Comparison of Machine Learning Algorithms for Discharge Prediction of Multipurpose Dam

Author

Jiyeong Hong, Seoro Lee, Gwanjae Lee, Dongseok Yang, Joo Hyun Bae, Jonggun Kim, Kisung Kim, and Kyoung Jae Lim

Subjects
dam discharge, decision tree, multilayer perceptron, K-nearest neighbor, support vector machine, random forest, Hydraulic engineering, TC1-978, Water supply for domestic and industrial purposes, TD201-500
Abstract

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.

Published
2021
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2. Comparison of Machine Learning Algorithms for Discharge Prediction of Multipurpose Dam

Author

Kyoung Jae Lim, Gwanjae Lee, Jonggun Kim, Jiyeong Hong, Dongseok Yang, Joo Hyun Bae, Ki-Sung Kim, and Seoro Lee

Subjects
K-nearest neighbor, Mean squared error, Geography, Planning and Development, Decision tree, Inflow, Aquatic Science, Machine learning, computer.software_genre, Biochemistry, decision tree, multilayer perceptron, support vector machine, TD201-500, Water Science and Technology, Physical model, Water supply for domestic and industrial purposes, business.industry, Hydraulic engineering, dam discharge, Random forest, Support vector machine, random forest, gradient boosting, extreme gradient boosting, Multilayer perceptron, Environmental science, Gradient boosting, Artificial intelligence, TC1-978, business, Algorithm, computer
Abstract

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.

Published
2021

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