Impact of Normalization and Input on ARMAX-ANN Model Performance in Suspended Sediment Load Prediction

The suspended sediment load in rivers is an important parameter in watershed planning and management. Since daily suspended sediment time series contain linear and nonlinear components, existing prediction models are associated with limitations. Therefore, this study introduces a new hybrid model co...

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Bibliographic Details
Published in:Water resources management 2018-02, Vol.32 (3), p.845-863
Main Authors: Moeeni, Hamid, Bonakdari, Hossein
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Atmospheric Sciences
Autoregressive models
Civil Engineering
Earth and Environmental Science
Earth Sciences
Environment
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Hydrology/Water Resources
Methods
Model accuracy
Modelling
Neural networks
Prediction models
Rivers
Sediment
Sediment load
Sediments
Stochasticity
Suspended load
Suspended sediments
Watersheds
White noise
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Summary:The suspended sediment load in rivers is an important parameter in watershed planning and management. Since daily suspended sediment time series contain linear and nonlinear components, existing prediction models are associated with limitations. Therefore, this study introduces a new hybrid model comprising two commonly used stochastic and nonlinear models. The sediment load is first modeled by an autoregressive-moving average with exogenous terms (ARMAX) model. Subsequently, the ARMAX residuals are modeled with an artificial neural network (ANN). For this purpose, discharge ( Q ) and sediment ( S ) are considered as model input parameters. Three modeling scenarios are defined to investigate the impact of data normalization on the hybrid model. The exponential and Box-Cox transformation methods are combined into a new data normalization method called mixed transformation. The performance of these methods is then compared. In addition, the impact of the type and number of input combinations on ARMAX-ANN model accuracy is evaluated. To this end, 12 input combinations and 1331 ARMAX and ANN models are verified. The ARMAX model inputs include S , Q and the white noise disturbance term ( e ), while the ANN model inputs include the ARMAX model results and residuals. Moreover, the hybrid model’s accuracy is compared with the ARMAX and ANN models.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-017-1842-z