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| Titel |
River flow time series using least squares support vector machines |
| VerfasserIn |
R. Samsudin, P. Saad, A. Shabri |
| 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 ; 15, no. 6 ; Nr. 15, no. 6 (2011-06-17), S.1835-1852 |
| Datensatznummer |
250012854
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| Publikation (Nr.) |
 copernicus.org/hess-15-1835-2011.pdf |
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| Zusammenfassung |
| This paper proposes a novel hybrid forecasting model known as GLSSVM, which
combines the group method of data handling (GMDH) and the least squares
support vector machine (LSSVM). The GMDH is used to determine the useful
input variables which work as the time series forecasting for the LSSVM
model. Monthly river flow data from two stations, the Selangor and Bernam
rivers in Selangor state of Peninsular Malaysia were taken into
consideration in the development of this hybrid model. The performance of
this model was compared with the conventional artificial neural network
(ANN) models, Autoregressive Integrated Moving Average (ARIMA), GMDH and
LSSVM models using the long term observations of monthly river flow
discharge. The root mean square error (RMSE) and coefficient of correlation
(R) are used to evaluate the models' performances. In both cases, the new
hybrid model has been found to provide more accurate flow forecasts compared
to the other models. The results of the comparison indicate that the new
hybrid model is a useful tool and a promising new method for river flow
forecasting. |
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