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| Titel |
Multi-criteria validation of artificial neural network rainfall-runoff modeling |
| VerfasserIn |
R. Modarres |
| 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 ; 13, no. 3 ; Nr. 13, no. 3 (2009-03-19), S.411-421 |
| Datensatznummer |
250011804
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| Publikation (Nr.) |
 copernicus.org/hess-13-411-2009.pdf |
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| Zusammenfassung |
| In this study we propose a comprehensive multi-criteria validation test for
rainfall-runoff modeling by artificial neural networks. This study applies
17 global statistics and 3 additional non-parametric tests to evaluate the
ANNs. The weakness of global statistics for validation of ANN is
demonstrated by rainfall-runoff modeling of the Plasjan Basin in the western
region of the Zayandehrud watershed, Iran. Although the global statistics
showed that the multi layer perceptron with 4 hidden layers (MLP4) is the
best ANN for the basin comparing with other MLP networks and empirical
regression model, the non-parametric tests illustrate that neither the ANNs
nor the regression model are able to reproduce the probability distribution
of observed runoff in validation phase. However, the MLP4 network is the
best network to reproduce the mean and variance of the observed runoff based
on non-parametric tests. The performance of ANNs and empirical model was
also demonstrated for low, medium and high flows. Although the MLP4 network
gives the best performance among ANNs for low, medium and high flows based
on different statistics, the empirical model shows better results. However,
none of the models is able to simulate the frequency distribution of low,
medium and high flows according to non-parametric tests. This study
illustrates that the modelers should select appropriate and relevant
evaluation measures from the set of existing metrics based on the particular
requirements of each individual applications. |
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