Water Resources Management

, Volume 32, Issue 5, pp 1883–1899 | Cite as

Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons

  • Zaher Mundher Yaseen
  • Minglei Fu
  • Chen Wang
  • Wan Hanna Melini Wan Mohtar
  • Ravinesh C. Deo
  • Ahmed El-shafie
Article
  • 46 Downloads

Abstract

Streamflow forecasting is paramount process in water and flood management, determination of river water flow potentials, environmental flow analysis, agricultural practices and hydro-power generation. However, the dynamicity, stochasticity and inherent complexities present in the temporal evolution of streamflow could hinder the accurate and reliable forecasting of this important hydrological parameter. In this study, the uncertainty and nonstationary characteristics of streamflow data has been treated using a set of coupled data pre-processing methods before being considered as input for an artificial neural network algorithm namely; rolling mechanism (RM) and grey models (GM). The rolling mechanism method is applied to smooth out the dataset based on the antecedent values of the model inputs before being applied to the GM algorithm. The optimization of the input datasets selection was performed using auto-correlation (ACF) and partial auto-correlation (PACF) functions. The pre-processed data was then integrated with two artificial neural network models, the back propagation (RMGM-BP) and Elman Recurrent Neural Network (RMGM-ERNN). The development, training, testing and evaluation of the proposed hybrid models were undertaken using streamflow data for two tropical hydrological basins (Johor and Kelantan Rivers). The hybrid RMGM-ERNN was found to provide better results than the hybrid RMGM-BP model. Relatively good performance of the proposed hybrid models with a data pre-processing approach provides a successful alternative to achieve better accuracy in streamflow forecasting compared to the traditional artificial neural network approach without a data pre-processing scheme.

Keywords

Rolling mechanism Grey model Artificial neural network Streamflow Tropical environment Multiple time scales 

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Zaher Mundher Yaseen
    • 1
  • Minglei Fu
    • 2
  • Chen Wang
    • 2
  • Wan Hanna Melini Wan Mohtar
    • 3
  • Ravinesh C. Deo
    • 4
  • Ahmed El-shafie
    • 5
  1. 1.Faculty of Civil EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam
  2. 2.College of ScienceZhejiang University of TechnologyHangzhouChina
  3. 3.Department of Civil and Structural Engineering, Faculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia, UKMBangiMalaysia
  4. 4.School of Agricultural Computational and Environmental Sciences, International Centre of Applied Climate Science (ICACS)University of Southern QueenslandSpringfieldAustralia
  5. 5.Civil Engineering Department, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia

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