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| Titel |
Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012) |
| VerfasserIn |
F. Fahimi, A. H. El-Shafie |
| 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 ; 18, no. 7 ; Nr. 18, no. 7 (2014-07-29), S.2711-2714 |
| Datensatznummer |
250120417
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| Publikation (Nr.) |
 copernicus.org/hess-18-2711-2014.pdf |
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| Zusammenfassung |
| Without a doubt, river flow forecasting is one of the most important issues
in water engineering field. There are lots of forecasting techniques that
have successfully been utilized by previously conducted studies in water
resource management and water engineering. The study of Ismail et al. (2012), which
was published in the journal Hydrology and Earth System Sciences in
2012, was a valuable piece of research that investigated the combination of two effective
methods (self-organizing map and least squares support vector machine) for
river flow forecasting. The goal was to make a comparison between the
performances of self organizing map and least square support vector machine
(SOM-LSSVM), autoregressive integrated moving average (ARIMA),
artificial neural network (ANN) and least squares support vector machine
(LSSVM) models for river flow prediction. This comment attempts to focus on
some parts of the original paper that need more discussion. The emphasis
here is to provide more information about the accuracy of the observed river
flow data and the optimum map size for SOM mode as well. |
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