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| Titel |
River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin |
| VerfasserIn |
M. K. Akhtar, G. A. Corzo, S. J. Andel, A. Jonoski |
| 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. 9 ; Nr. 13, no. 9 (2009-09-10), S.1607-1618 |
| Datensatznummer |
250011992
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| Publikation (Nr.) |
 copernicus.org/hess-13-1607-2009.pdf |
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| Zusammenfassung |
| This paper explores the use of flow length and travel time as a
pre-processing step for incorporating spatial precipitation information into
Artificial Neural Network (ANN) models used for river flow forecasting.
Spatially distributed precipitation is commonly required when modelling large
basins, and it is usually incorporated in distributed physically-based
hydrological modelling approaches. However, these modelling approaches are
recognised to be quite complex and expensive, especially due to the data
collection of multiple inputs and parameters, which vary in space and time.
On the other hand, ANN models for flow forecasting are frequently developed
only with precipitation and discharge as inputs, usually without taking into
consideration the spatial variability of precipitation. Full inclusion of
spatially distributed inputs into ANN models still leads to a complex
computational process that may not give acceptable results. Therefore, here
we present an analysis of the flow length and travel time as a basis for
pre-processing remotely sensed (satellite) rainfall data. This pre-processed
rainfall is used together with local stream flow measurements of previous
days as input to ANN models. The case study for this modelling approach is
the Ganges river basin. A comparative analysis of multiple ANN models with
different hydrological pre-processing is presented. The ANN showed its
ability to forecast discharges 3-days ahead with an acceptable accuracy.
Within this forecast horizon, the influence of the pre-processed rainfall is
marginal, because of dominant influence of strongly auto-correlated discharge
inputs. For forecast horizons of 7 to 10 days, the influence of the
pre-processed rainfall is noticeable, although the overall model performance
deteriorates. The incorporation of remote sensing data of spatially
distributed precipitation information as pre-processing step showed to be a
promising alternative for the setting-up of ANN models for river flow
forecasting. |
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