Predictive model-based for the critical submergence of horizontal intakes in open channel flows with different clearance bottoms using CART, ANN and linear regression approaches

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This study presents the development of classification and regression tree (CART), artificial neural network (ANN) and linear regression approaches to predict the critical submergence in an open channel flow for different clearance bottoms. To use the models for application purposes and cover the wide range of inputs, the nondimensional parameters are employed to train and test. The testing results show that all three approaches satisfactorily estimate the critical submergence with margin differences. Also, committee models arithmetic mean-based for the testing results of the tree mentioned approaches are presented as the best models. A comparison between the present study and empirical approaches is carried out which indicates the proposed approaches outperform the empirical formulas expressed in the literature. In addition, committee models are presented as the more generalized approaches by AIC criterion. The results also indicate that the variations of the best approach (committee)-predicted and observed the normalized critical submergence with the intake pipe diameter versus the number of the testing data follow favorably a similar trend. Finally, a sensitivity analysis shows that the ratio of the velocity in an intake pipe to the velocity in a channel is the significant parameter in the estimation of the critical submergence.