Application of Artificial Neural Network to Forecast the Water Level at Xuan Quan Culvert in Vietnam

  • Truong Viet Hoang
  • Doan Mai Huong
  • Tran Duc HuyEmail author
  • Hung Viet Ho
Conference paper


Artificial intelligence is increasingly widely used in many different areas of life, including irrigation. In recent years, Machine learning and Artificial neural networks (ANN) models are excellent tools for forecasting flow rate in the river or the water levels at irrigation culverts to help the operation of the sluice gates be appropriate and effective. In this study, a simple and efficient model based on ANN is suggested for water level prediction at Xuan Quan culvert in Vietnam for 6 hours, 12 hours, 18 hours and 24 hours of lead time. The input data needed for prediction are observed water levels in the upstream and downstream of the Xuan Quan culvert, from 2003 to 2018. Although the model does not require a lot of data, the forecasted results are highly accurate. The Nash–Sutcliffe efficiency (NSE) reaches 97% - 99% for all forecasting cases. This result shows that the ANN model proposed by the research team accurately predicts water levels in real time, furthermore, this model can be applied to forecast water levels at other irrigation sluice gates.


ANN LSTM Xuan Quan water level forecast 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Akhilesh (2019). Correlation Coefficient. Retrieved from
  2. Asaad, Y. S. (2010). Artificial neural network model for river flow forecasting in a developing country. Journal of Hydroinformatics, 12.1.Google Scholar
  3. Chen, J.F., Hsieh, H.N., and Do, Q.H. (2014). Forecasting Hoabinh Reservoir’s Incoming Flow: An Application of Neural Networks with the Cuckoo Search Algorithm. Information. 5, 570-586.CrossRefGoogle Scholar
  4. Le, X.H., and Ho, V.H. (2018). Application of Long Short-term Memory to forecast water level at Quang Phuc and Cua Cam Stations at Hai Phong, Viet Nam. Journal of Water Resources and Environmental Engineering.Google Scholar
  5. M. R. Mustafa, M. H. Isa, R. B. Rezaur (2012). Artificial Neural Networks Modeling in Water Resources Engineering: Infrastructure and Applications. World Academy of Science, Engineering and Technology International Journal of Civil and Environmental Engineering, 6(2).Google Scholar
  6. Nguyen, D.T. (2003). Using Artificial Neural Network to Forecast Rainfall and Discharge to Support The Drought Preparedness and Mitigation Measures in the Central Highlands – Vietnam. Journal of Water Resources and Environmental Engineering.Google Scholar
  7. Shilpi Rani, Falguni Parekh (2012). Application of Artificial Neural Network (ANN) for Reservoir Water Level Forecasting. International Journal of Science and Research (IJSR).Google Scholar
  8. Zaheer, I., Bai, C. G. (2003). Application of Artificial Neural Network for Water Quality Management. Lowland Technology International, 5(2), 10-15.Google Scholar
  9. Olah, C. (2015). Understanding LSTM Networks. Retrieved from

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Truong Viet Hoang
    • 1
  • Doan Mai Huong
    • 1
  • Tran Duc Huy
    • 1
    Email author
  • Hung Viet Ho
    • 2
  1. 1.Department of Advanced ProgramThuyloi UniversityHanoi CityVietnam
  2. 2.Faculty of Water Resources EngineeringThuyloi UniversityHanoi CityVietnam

Personalised recommendations