Sudden diverging channels are one of the energy dissipaters which can dissipate most of the kinetic energy of the flow through a hydraulic jump. An accurate prediction of hydraulic jump characteristics is an important step in designing hydraulic structures. This paper focuses on the capability of the support vector machine (SVM) as a meta-model approach for predicting hydraulic jump characteristics in different sudden diverging stilling basins (i.e. basins with and without appurtenances). In this regard, different models were developed and tested using 1,018 experimental data. The obtained results proved the capability of the SVM technique in predicting hydraulic jump characteristics and it was found that the developed models for a channel with a central block performed more successfully than models for channels without appurtenances or with a negative step. The superior performance for the length of hydraulic jump was obtained for the model with parameters F1 (Froude number) and (h2—h1)/h1 (h1 and h2 are sequent depth of upstream and downstream respectively). Concerning the relative energy dissipation and sequent depth ratio, the model with parameters F1 and h1/B (B is expansion ratio) led to the best results. According to the outcome of sensitivity analysis, Froude number had the most significant effect on the modeling. Also comparison between SVM and empirical equations indicated the great performance of the SVM.

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