Articles | Volume 18, issue 7
Hydrol. Earth Syst. Sci., 18, 2711–2714, 2014
https://doi.org/10.5194/hess-18-2711-2014
Hydrol. Earth Syst. Sci., 18, 2711–2714, 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 F. Fahimi and A. H. El-Shafie
  • Department of Civil & Structural Engineering, Universiti Kebangsaan Malaysia, UKM, Bangi, Selangor, Malaysia

Abstract. 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.