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http://dx.doi.org/10.3741/JKWRA.2021.54.S-1.1095

Application of deep learning method for decision making support of dam release operation  

Jung, Sungho (Department of Advanced Science and Technology Convergence, Kyungpook National University)
Le, Xuan Hien (Disaster Prevention Emergency Management Institute, Kyungpook National University)
Kim, Yeonsu (Department of Water Resources Research Management, K-water Research Institute)
Choi, Hyungu (Nakdonggang River Basin Head Office, K-water)
Lee, Giha (Department of Advanced Science and Technology Convergence, Kyungpook National University)
Publication Information
Journal of Korea Water Resources Association / v.54, no.spc1, 2021 , pp. 1095-1105 More about this Journal
Abstract
The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.
Keywords
Nam river dam; Dam operation; Water level prediction; Deep learning; LSTM model;
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