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| Titel |
Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks |
| VerfasserIn |
J. C. Ochoa-Rivera, R. García-Bartual, J. Andreu |
| 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 ; 6, no. 4 ; Nr. 6, no. 4, S.641-654 |
| Datensatznummer |
250003663
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| Publikation (Nr.) |
 copernicus.org/hess-6-641-2002.pdf |
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| Zusammenfassung |
| A model for multivariate
streamflow generation is presented, based on a multilayer feedforward neural
network. The structure of the model results from two components, the neural
network (NN) deterministic component and a random component which is assumed to
be normally distributed. It is from this second component that the model
achieves the ability to incorporate effectively the uncertainty associated with
hydrological processes, making it valuable as a practical tool for synthetic
generation of streamflow series. The NN topology and the corresponding
analytical explicit formulation of the model are described in detail. The model
is calibrated with a series of monthly inflows to two reservoir sites located in
the Tagus River basin (Spain), while validation is performed through estimation
of a set of statistics that is relevant for water resources systems planning and
management. Among others, drought and storage statistics are computed and
compared for both the synthetic and historical series. The performance of the NN-based
model was compared to that of a standard autoregressive AR(2) model. Results
show that NN represents a promising modelling alternative for simulation
purposes, with interesting potential in the context of water resources systems
management and optimisation.
Keywords: neural networks, perceptron multilayer, error backpropagation,
hydrological scenario generation, multivariate time-series. . |
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