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Open AccessArticle

Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow

1
Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Korea
2
Korea Water Environment Research Institute, Chuncheon-si 24408, Korea
*
Author to whom correspondence should be addressed.
Water 2020, 12(10), 2927; https://doi.org/10.3390/w12102927
Received: 3 September 2020 / Revised: 14 October 2020 / Accepted: 15 October 2020 / Published: 20 October 2020
(This article belongs to the Section Hydraulics and Hydrodynamics)
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. View Full-Text
Keywords: dam inflow; decision tree; multilayer perceptron; random forest; gradient boosting; RNN–LSTM; CNN–LSTM dam inflow; decision tree; multilayer perceptron; random forest; gradient boosting; RNN–LSTM; CNN–LSTM
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MDPI and ACS Style

Hong, J.; Lee, S.; Bae, J.H.; Lee, J.; Park, W.J.; Lee, D.; Kim, J.; Lim, K.J. Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow. Water 2020, 12, 2927. https://doi.org/10.3390/w12102927

AMA Style

Hong J, Lee S, Bae JH, Lee J, Park WJ, Lee D, Kim J, Lim KJ. Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow. Water. 2020; 12(10):2927. https://doi.org/10.3390/w12102927

Chicago/Turabian Style

Hong, Jiyeong; Lee, Seoro; Bae, Joo H.; Lee, Jimin; Park, Woon J.; Lee, Dongjun; Kim, Jonggun; Lim, Kyoung J. 2020. "Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow" Water 12, no. 10: 2927. https://doi.org/10.3390/w12102927

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