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http://dx.doi.org/10.3741/JKWRA.2021.54.3.157

Application of sequence to sequence learning based LSTM model (LSTM-s2s) for forecasting dam inflow  

Han, Heechan (Department of Civil and Environmental Engineering, Colorado State University)
Choi, Changhyun (Risk Management Office, KB Claims Survey and Adjusting)
Jung, Jaewon (Institute of Water Resources System, Inha University)
Kim, Hung Soo (Department of Civil Engineering, Inha University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.3, 2021 , pp. 157-166 More about this Journal
Abstract
Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.
Keywords
Dam inflow forecasting; Deep learning; LSTM with Sequence-to-Sequence learning;
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