Articles | Volume 18, issue 7
https://doi.org/10.5194/hess-18-2711-2014
https://doi.org/10.5194/hess-18-2711-2014
Comment/reply
 | 
29 Jul 2014
Comment/reply |  | 29 Jul 2014

Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012)

F. Fahimi and A. H. El-Shafie

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Cited articles

Abrahart, R. J. and See, L.: Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments, Hydrol. Process., 14, 2157–2172, 2000.
Alhoniemi, E., Hollmen, J., Simula, O., and Vesanto, J.: Process monitoring and modeling using the self-organizing map, Integr. Comp.-Aid. Eng., 6, 3–14, 1999.
Chon, T. S., Park, Y. S., Moon, K. H., and Cha, E. Y.: Patternizing communities by using an artificial neural network, Ecol. Model., 90, 69–78, 1996.
Ismail, S., Shabri, A., and Samsudin, R.: A hybrid model of self organizing maps and least square support vector machine for river flow forecasting, Hydrol. Earth Syst. Sci., 16, 4417–4433, https://doi.org/10.5194/hess-16-4417-2012, 2012.
Kohonen, T.: The self-organizing map, Neurocomputing, 21, 1–6, 1998.