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 and Ahmed El-shafie
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Zaher Mundher Yaseen: Ton Duc Thang University
Minglei Fu: Zhejiang University of Technology
Chen Wang: Zhejiang University of Technology
Wan Hanna Melini Wan Mohtar: Universiti Kebangsaan Malaysia, UKM
Ravinesh C. Deo: University of Southern Queensland
Ahmed El-shafie: University of Malaya

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 5, No 21, 1883-1899

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11269-018-1909-5

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