Abstract
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.
Highlights
Predicting river flow has become one of the indispensable parts of water resource management and water engineering
Ismail et al (2012) carried out an inclusive study to improve the forecasting of river flow by using four different methods, i.e., self-organizing map (SOM)-LSSVM, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and least squares support vector machine (LSSVM) models
The main contribution of this study was to improve the efficiency of the river flow prediction by employing a self-organizing map (SOM) model for clustering input data and coupling this method with LSSVM model
Summary
Predicting river flow has become one of the indispensable parts of water resource management and water engineering. Ismail et al (2012) carried out an inclusive study to improve the forecasting of river flow by using four different methods, i.e., SOM-LSSVM, autoregressive integrated moving average (ARIMA), ANN, and least squares support vector machine (LSSVM) models. The main contribution of this study was to improve the efficiency of the river flow prediction by employing a self-organizing map (SOM) model for clustering input data and coupling this method with LSSVM model. They examined 516 monthly recorded flow data of Bernam River in Malaysia from the beginning of 1966 to the end of 2008.
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