Application of the hybrid artificial neural network coupled with rolling mechanism and grey model algorithms for streamflow forecasting over multiple time horizons

Yaseen, Zaher Mundher and Fu, Minglei and Wang, Chen and Mohtar, Wan Hanna Melini Wan and Deo, Ravinesh C. and El-Shafie, Ahmed (2018) Application of the hybrid artificial neural network coupled with rolling mechanism and grey model algorithms for streamflow forecasting over multiple time horizons. Water Resources Management. ISSN 0920-4741

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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published online 19 January 2018. Permanent restricted access to ArticleFirst version, in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 24 Jan 2018 01:47
Last Modified: 14 May 2018 04:21
Uncontrolled Keywords: rolling mechanism; grey model; artificial neural network; streamflow; tropical environment; multiple time scales
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
04 Earth Sciences > 0401 Atmospheric Sciences > 040104 Climate Change Processes
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
Identification Number or DOI: 10.1007/s11269-018-1909-5
URI: http://eprints.usq.edu.au/id/eprint/33617

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