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| Titel |
Hydrological model coupling with ANNs |
| VerfasserIn |
R. G. Kamp, H. H. G. Savenije |
| 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 ; 11, no. 6 ; Nr. 11, no. 6 (2007-12-04), S.1869-1881 |
| Datensatznummer |
250009549
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| Publikation (Nr.) |
 copernicus.org/hess-11-1869-2007.pdf |
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| Zusammenfassung |
There is an increasing need for model coupling. However, model coupling is
complicated. Scientists develop and improve models to represent physical
processes occurring in nature. These models are built in different software
programs required to run the model. A software program or application
represents part of the system knowledge. This knowledge is however
encapsulated in the program and often difficult to access.
In integrated water resources management it is often necessary to connect
hydrological, hydraulic or ecological models. Model coupling can in practice
be difficult for many reasons related to data formats, compatibility of
scales, ability to modify source codes, etc. Hence, there is a need for an
efficient and cost effective approach to model-coupling. Artificial neural
networks (ANNs) can be used as an alternative to replace a model and simulate
the model's output and connect it to other models.
In this paper, we investigate an alternative to traditional model coupling
techniques. ANNs are four different models: a rainfall runoff model, a river
channel routing model, an estuarine salt intrusion model, and an ecological
model. The output results of each model is simulated by a neural network that
is trained on corresponding input and output data sets. The models are
connected in cascade and their input and output variables are connected.
To test the results of the coupled neural network also a coupled system of
four sub-system models has been set-up. These results have been compared to
the results of the coupled neural networks. The results show that it is
possible to train neural networks and connect these models. The results of
the salt intrusion model was however not very accurate. It was difficult for
the neural network to represent both short term (tidal) and long term
(hydrological) processes. |
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