River streamflow prediction using a deep neural network: a case study on the Red River, Vietnam

  • Le, Xuan-Hien (Dept. of Disaster Prevention and Environmental Engineering, Kyungpook National University) ;
  • Ho, Hung Viet (Faculty of Water Resources Engineering, Thuyloi University) ;
  • Lee, Giha (Dept. of Disaster Prevention and Environmental Engineering, Kyungpook National University)
  • Received : 2019.05.22
  • Accepted : 2019.10.18
  • Published : 2019.12.31


Real-time flood prediction has an important role in significantly reducing potential damage caused by floods for urban residential areas located downstream of river basins. This paper presents an effective approach for flood forecasting based on the construction of a deep neural network (DNN) model. In addition, this research depends closely on the open-source software library, TensorFlow, which was developed by Google for machine and deep learning applications and research. The proposed model was applied to forecast the flowrate one, two, and three days in advance at the Son Tay hydrological station on the Red River, Vietnam. The input data of the model was a series of discharge data observed at five gauge stations on the Red River system, without requiring rainfall data, water levels and topographic characteristics. The research results indicate that the DNN model achieved a high performance for flood forecasting even though only a modest amount of data is required. When forecasting one and two days in advance, the Nash-Sutcliffe Efficiency (NSE) reached 0.993 and 0.938, respectively. The findings of this study suggest that the DNN model can be used to construct a real-time flood warning system on the Red River and for other river basins in Vietnam.


Supported by : Korea Environment Industry & Technology Institute(KEITI)


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