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, A. H. El-Shafie

    Research output: Contribution to journalArticle

    1 Citation (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)2711-2714
    Number of pages4
    JournalHydrology and Earth System Sciences
    Volume18
    Issue number7
    DOIs
    Publication statusPublished - 29 Jul 2014

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    river flow
    engineering
    artificial neural network
    hydrology
    water
    support vector machine
    prediction

    ASJC Scopus subject areas

    • Earth and Planetary Sciences (miscellaneous)
    • Water Science and Technology

    Cite this

    Comment on "A hybrid model of self organizing maps and least square support vector machine for river flow forecasting" by Ismail et al. (2012). / Fahimi, F.; El-Shafie, A. H.

    In: Hydrology and Earth System Sciences, Vol. 18, No. 7, 29.07.2014, p. 2711-2714.

    Research output: Contribution to journalArticle

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