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

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...

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Veröffentlicht in:	Water resources management Jg. 32; H. 5; S. 1883 - 1899
Hauptverfasser:	Yaseen, Zaher Mundher, Fu, Minglei, Wang, Chen, Mohtar, Wan Hanna Melini Wan, Deo, Ravinesh C., El-shafie, Ahmed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.03.2018 Springer Nature B.V
Schlagworte:	Agricultural practices Algorithms Artificial neural networks Atmospheric Sciences Autocorrelation Back propagation networks Basins Civil Engineering Correlation Data data collection Data processing Earth and Environmental Science Earth Sciences Electric power generation Environment Evaluation Flood control Flood forecasting Flood management Forecasting Geotechnical Engineering & Applied Earth Sciences Hydroelectric power Hydrogeology Hydrologic data Hydrologic models Hydrology Hydrology/Water Resources Mathematical models Neural networks Parameter uncertainty Recurrent neural networks River water Rivers Rolling (ship motion) Stochasticity Stream discharge Stream flow Streamflow forecasting Training Tropical climate uncertainty Water flow water power Malaysia Rolling mechanism Artificial neural network Multiple time scales Streamflow Grey model Tropical environment
ISSN:	0920-4741, 1573-1650
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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.
AbstractList	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.
Author	Yaseen, Zaher Mundher Deo, Ravinesh C. Wang, Chen Fu, Minglei Mohtar, Wan Hanna Melini Wan El-shafie, Ahmed
Author_xml	– sequence: 1 givenname: Zaher Mundher surname: Yaseen fullname: Yaseen, Zaher Mundher email: yaseen@tdt.edu.vn organization: Faculty of Civil Engineering, Ton Duc Thang University – sequence: 2 givenname: Minglei surname: Fu fullname: Fu, Minglei organization: College of Science, Zhejiang University of Technology – sequence: 3 givenname: Chen surname: Wang fullname: Wang, Chen organization: College of Science, Zhejiang University of Technology – sequence: 4 givenname: Wan Hanna Melini Wan surname: Mohtar fullname: Mohtar, Wan Hanna Melini Wan organization: Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, UKM – sequence: 5 givenname: Ravinesh C. surname: Deo fullname: Deo, Ravinesh C. organization: School of Agricultural Computational and Environmental Sciences, International Centre of Applied Climate Science (ICACS), University of Southern Queensland – sequence: 6 givenname: Ahmed surname: El-shafie fullname: El-shafie, Ahmed organization: Civil Engineering Department, Faculty of Engineering, University of Malaya
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Keywords	Rolling mechanism Artificial neural network Multiple time scales Streamflow Grey model Tropical environment
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SubjectTerms	Agricultural practices Algorithms Artificial neural networks Atmospheric Sciences Autocorrelation Back propagation networks Basins Civil Engineering Correlation Data data collection Data processing Earth and Environmental Science Earth Sciences Electric power generation Environment Evaluation Flood control Flood forecasting Flood management Forecasting Geotechnical Engineering & Applied Earth Sciences Hydroelectric power Hydrogeology Hydrologic data Hydrologic models Hydrology Hydrology/Water Resources Mathematical models Neural networks Parameter uncertainty Recurrent neural networks River water Rivers Rolling (ship motion) Stochasticity Stream discharge Stream flow Streamflow forecasting Training Tropical climate uncertainty Water flow water power
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Title	Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons
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