Prediction of multipurpose dam inflow using deep learning

Ji Yoon Mok, Ji Hyeok Choi, Young Il Moon

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

Original languageEnglish
Pages (from-to)97-105
Number of pages9
JournalJournal of Korea Water Resources Association
Issue number2
StatePublished - Feb 2020


  • Deep learning
  • LSTM
  • Multipurpose dam
  • Prediction of inflow


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