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Without a doubt, river flow forecasting is one of the most important issuesin water engineering field. There are lots of forecasting techniques thathave successfully been utilized by previously conducted studies in waterresource management and water engineering. The study of Ismail et al. (2012), whichwas published in the journal <i>Hydrology and Earth System Sciences</i> in2012, was a valuable piece of research that investigated the combination of two effectivemethods (self-organizing map and least squares support vector machine) forriver flow forecasting. The goal was to make a comparison between theperformances 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 onsome parts of the original paper that need more discussion. The emphasishere is to provide more information about the accuracy of the observed riverflow data and the optimum map size for SOM mode as well

hydrology and earth system sciences

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