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)


  1. Aichouri I, Hani A, Bougherira N, Djabri L, Chaffai H, Lallahem S. 2015. River flow model using artificial neural networks. Energy Procedia 74:1007-1014.
  2. Chen JF, Hsieh HN, Do Q. 2014. Forecasting Hoabinh reservoir's incoming flow: An application of neural networks with the cuckoo search algorithm. Information 5:570.
  3. Elsafi SH. 2014. Artificial neural networks (ANNs) for flood forecasting at Dongola station in the River Nile, Sudan. Alexandria Engineering Journal 53:655-662.
  4. Govindaraju RS. 2000a. Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering 5:115-123.
  5. Govindaraju RS. 2000b. Artificial neural networks in hydrology. II: Hydrologic applications. Journal of Hydrologic Engineering 5:124-137.
  6. Hidayat H, Hoitink AJF, Sassi MG, Torfs PJJF. 2014. Prediction of discharge in a tidal river using artificial neural networks. Journal of Hydrologic Engineering 19:04014006.
  7. Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Computation 9:1735-1780.
  8. Kingma DP, Ba J. 2014. Adam: A method for stochastic optimization. CoRR abs/1412.6980.
  9. Kisi O. 2011. A combined generalized regression neural network wavelet model for monthly streamflow prediction. KSCE Journal of Civil Engineering 15:1469-1479.
  10. Le XH, Ho HV. 2018. Using long short-term memory neural network to forecast water level at the Quang Phuc and the Cua Cam stations in Hai Phong, Vietnam. Journal of Water Resources & Environmental Engineering 62:9-16. [in Vietnamese]
  11. Le XH, Ho HV, Lee G, Jung S. 2018. A deep neural network application for forecasting the inflow into the Hoa Binh reservoir in Vietnam. 11th International Symposium on Lowland Technology (ISLT 2018), 26-28 September 2018.
  12. Le XH, Ho HV, Lee G, Jung S. 2019. Application of long short-term memory (LSTM) neural network for flood forecasting. Water 11:1387.
  13. LeCun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature 521:436.
  14. McKinney W. 2010. Data structures for statistical computing in Python. pp. 51-56. 9th Python in Science Conference, Austin, USA.
  15. Nash JE, Sutcliffe JV. 1970. River flow forecasting through conceptual models Part I - A discussion of principles. Journal of Hydrologic Engineering 10:282-290.
  16. Nguyen TT. 2015. An L1-regression random forests method for forecasting of Hoa Binh reservoir's incoming flow. pp. 360-364. 7th International Conference on Knowledge and Systems Engineering (KSE), 8-10 October 2015.
  17. Nguyen TT, Nguyen HQ, Li MJ. 2015. Forecasting time series water levels on Mekong River using machine learning models. pp. 292-297. 7th International Conference on Knowledge and Systems Engineering (KSE), 8-10 Oct. 2015.
  18. Olah C. 2015. Understanding LSTM networks. Accessed in on 28 June 2018.
  19. Rossum G. 1995. Python tutorial. CWI (Centre for Mathematics and Computer Science), Netherlands.
  20. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research 15:1929-1958.
  21. Sung J, Lee J, Chung IM, Heo JH. 2017. Hourly water level forecasting at tributary affected by main river condition. Water 9:644.
  22. Truong XN, Nguyen TT. 2016. Deep learning: Application to forecast incoming flow of Hoa Binh reservoir. pp. 189-191. The Annual Conference of Thuyloi University, Hanoi, Vietnam, 18 November 2016. [in Vietnamese]
  23. Van Der Walt S, Colbert SC, Varoquaux G. 2011. The NumPy array: A structure for efficient numerical computation. Computing in Science & Engineering 13:22-30.
  24. Veintimilla-Reyes J, Cisneros F, Vanegas P. 2016. Artificial neural networks applied to flow prediction: A use case for the Tomebamba River. Procedia Engineering 162:153-161.
  25. Wang J, Shi P, Jiang P, Hu J, Qu S, Chen X, Chen Y, Dai Y, Xiao Z. 2017. Application of BP neural network algorithm in traditional hydrological model for flood forecasting. Water 9:48.
  26. Seo Y, Kim S, Kisi O, Singh VP. 2015. Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. Journal of Hydrology 520:224-243.