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| Titel |
The use of artificial neural networks to analyze and predict alongshore sediment transport |
| VerfasserIn |
B. Maanen, G. Coco, K. R. Bryan, B. G. Ruessink |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 17, no. 5 ; Nr. 17, no. 5 (2010-09-02), S.395-404 |
| Datensatznummer |
250013720
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| Publikation (Nr.) |
 copernicus.org/npg-17-395-2010.pdf |
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| Zusammenfassung |
| An artificial neural network (ANN) was developed to predict the
depth-integrated alongshore suspended sediment transport rate using 4 input
variables (water depth, wave height and period, and alongshore velocity).
The ANN was trained and validated using a dataset obtained on the intertidal
beach of Egmond aan Zee, the Netherlands. Root-mean-square deviation between
observations and predictions was calculated to show that, for this specific
dataset, the ANN (εrms=0.43) outperforms the commonly used
Bailard (1981) formula (εrms=1.63), even when this formula
is calibrated (εrms=0.66). Because of correlations between
input variables, the predictive quality of the ANN can be improved further
by considering only 3 out of the 4 available input variables (εrms=0.39). Finally, we use the partial derivatives method to "open
and lighten" the generated ANNs with the purpose of showing that, although
specific to the dataset in question, they are not "black-box" type models
and can be used to analyze the physical processes associated with alongshore
sediment transport. In this case, the alongshore component of the velocity,
by itself or in combination with other input variables, has the largest
explanatory power. Moreover, the behaviour of the ANN indicates that
predictions can be unphysical and therefore unreliable when the input lies
outside the parameter space over which the ANN has been developed. Our
approach of combining the strong predictive power of ANNs with
"lightening" the black box and testing its sensitivity, demonstrates that
the use of an ANN approach can result in the development of generalized
models of suspended sediment transport. |
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