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Prediction of Water Level Using Time Series, Wavelet and Neural Network Approaches

Author

Listed:
  • Nguyen Quang Dat

    (Hanoi University of Science and Technology, Hanoi, Vietnam)

  • Ngoc Anh Nguyen Thi

    (Hanoi University of Science and Technology, Hanoi, Vietnam)

  • Vijender Kumar Solanki

    (CMR institute of Technology (Autonomous), Hyderabad, India)

  • Ngo Le An

    (Thuyloi University, Hanoi, Vietnam)

Abstract

To control water resources in many domains such as agriculture, flood forecasting, and hydro-electrical dams, forecasting water level needs to predict. In this article, a new computational approach using a data driven model and time series is proposed to calculate the forecast water level in short time. Concretely, wavelet-artificial neural network (WAANN) and time series (TS) are combined together called WAANN-TS that encourages the advantage of each model. For this real time project work, Yen Bai station, Northwest Vietnam was chosen as an experimental case study to apply the proposed model. Input variables into the Wavelet-ANN structure is water level data. Time series and ANN models are built, and their performances are compared. The results indicate the greater accuracy of the proposed models at Hanoi station. The final proposal WAANN−TS for water level forecasting shows good performance with root mean square error (RMSE) from 10−10 to 10−11.

Suggested Citation

  • Nguyen Quang Dat & Ngoc Anh Nguyen Thi & Vijender Kumar Solanki & Ngo Le An, 2020. "Prediction of Water Level Using Time Series, Wavelet and Neural Network Approaches," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 10(3), pages 1-19, July.
    • Handle: RePEc:igg:jirr00:v:10:y:2020:i:3:p:1-19
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