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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Vydáno v:Water resources management Ročník 32; číslo 5; s. 1883 - 1899
Hlavní autoři: Yaseen, Zaher Mundher, Fu, Minglei, Wang, Chen, Mohtar, Wan Hanna Melini Wan, Deo, Ravinesh C., El-shafie, Ahmed
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.03.2018
Springer Nature B.V
Témata:
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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Shrnutí: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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ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-018-1909-5