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| Titel |
Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia |
| VerfasserIn |
A. El-Shafie, A. Noureldin, M. Taha, A. Hussain, M. Mukhlisin |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 16, no. 4 ; Nr. 16, no. 4 (2012-04-10), S.1151-1169 |
| Datensatznummer |
250013255
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| Publikation (Nr.) |
 copernicus.org/hess-16-1151-2012.pdf |
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| Zusammenfassung |
Rainfall is considered as one of the major components of the hydrological
process; it takes significant part in evaluating drought and flooding
events. Therefore, it is important to have an accurate model for rainfall
forecasting. Recently, several data-driven modeling approaches have been
investigated to perform such forecasting tasks as multi-layer perceptron
neural networks (MLP-NN). In fact, the rainfall time series modeling
involves an important temporal dimension. On the other hand, the classical
MLP-NN is a static and has a memoryless network architecture that is effective for
complex nonlinear static mapping. This research focuses on investigating the
potential of introducing a neural network that could address the temporal
relationships of the rainfall series.
Two different static neural networks and one dynamic neural network, namely
the multi-layer perceptron neural network (MLP-NN), radial basis function neural
network (RBFNN) and input delay neural network (IDNN), respectively, have
been examined in this study. Those models had been developed for the two
time horizons for monthly and weekly rainfall forecasting at Klang River,
Malaysia. Data collected over 12 yr (1997–2008) on a weekly basis and 22 yr (1987–2008) on a monthly basis were used to develop and examine the
performance of the proposed models. Comprehensive comparison analyses were
carried out to evaluate the performance of the proposed static and dynamic
neural networks. Results showed that the MLP-NN neural network model is able to
follow trends of the actual rainfall, however, not very accurately. RBFNN
model achieved better accuracy than the MLP-NN model. Moreover, the
forecasting accuracy of the IDNN model was better than that of static
network during both training and testing stages, which proves a
consistent level of accuracy with seen and unseen data. |
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