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Incorporation of ARMA models into flow forecasting by artificial neural networks
10.1002/env.596.abs
A frequently encountered problem during the application of artificial neural networks (ANNs) to various water resource problems is the limitation of the data sets required for the training stage of ANNs. This prevents ANNs from learning input and output data sets within different ranges, thus decreasing the prediction capability during the testing stage. In this article the well known ARMA models are used to generate synthetic series, and these series are incorporated into the training data sets of ANNs. The method is applied to the monthly mean river flow data of a station in the East Mediterranean region of Turkey. The forecasting accuracy of the future monthly flows carries significance because a water reservoir is planned for the downstream of this station. Because the available data record length is limited studies should be carried out to extend the training data set of ANNs. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting using ANNs. The introduction of periodicity components in the input layer also increases the forecasting accuracy of ANNs. Copyright © 2003 John Wiley & Sons, Ltd.
Incorporation of ARMA models into flow forecasting by artificial neural networks
10.1002/env.596.abs
A frequently encountered problem during the application of artificial neural networks (ANNs) to various water resource problems is the limitation of the data sets required for the training stage of ANNs. This prevents ANNs from learning input and output data sets within different ranges, thus decreasing the prediction capability during the testing stage. In this article the well known ARMA models are used to generate synthetic series, and these series are incorporated into the training data sets of ANNs. The method is applied to the monthly mean river flow data of a station in the East Mediterranean region of Turkey. The forecasting accuracy of the future monthly flows carries significance because a water reservoir is planned for the downstream of this station. Because the available data record length is limited studies should be carried out to extend the training data set of ANNs. It is seen that the extension of input and output data sets in the training stage improves the accuracy of forecasting using ANNs. The introduction of periodicity components in the input layer also increases the forecasting accuracy of ANNs. Copyright © 2003 John Wiley & Sons, Ltd.
Incorporation of ARMA models into flow forecasting by artificial neural networks
Cigizoglu, H. K. (author)
Environmetrics ; 14 ; 417-427
2003-06-01
11 pages
Article (Journal)
Electronic Resource
English
Incorporation of ARMA models into flow forecasting by artificial neural networks
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