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 F (Froude number) and (hh)/h (h and h are sequent depth of upstream and downstream respectively). Concerning the relative energy dissipation and sequent depth ratio, the model with parameters F and h/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.

Hydraulic jump is a phenomenon which is used to dissipate the kinetic energy of the flow and prevent scour below overflow spillways, chutes and sluices. This paper applies adaptive neuro-fuzzy inference system (ANFIS) as a Meta model approach to estimate hydraulic jump characteristics in channels with different bed conditions (i.e. channels with different shapes and appurtenances). In hydraulic jump characteristics modeling, different input combinations were developed and tested using 1700 experimental data. The obtained results indicated that the applied method has high capability in modeling hydraulic jump characteristics. It was observed that the developed models for expanding channel with a block performed more successful than other channels. For rectangular channels, it was found that the basin with rough bed led to better predictions compared to the basin with a step. In the prediction of jump length, the superior performance was obtained for the model with input combinations of Froude number and the relative height of jump. From the sensitivity analysis, it was induced that, Fr1 (upstream Froude number) is the most significant parameter in modeling process. Also comparison between ANFIS and semi-empirical equations indicated the great performance of the ANFIS.

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