Application of computational intelligence methods for complex two-phase flow pattern recognition

Recognizing the significance of accurate predictions of flow conditions in open-channel bends is key in attempts to preserve riverbanks from erosion. The focus of the present work is on the application of two computational intelligence (CI) models to predict free surface flow pattern in a strongly curved 60 degrees open-channel bend. An experimental study is also carried out to prepare the required input and output data set for the CI training and validation process. A set of 476 data is used to train and test the proposed models. The adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) networks are adopted to construct the models. The CI results are compared with the experimental data obtained. Excellent agreement is found representing the reliability of the employed models. The findings confirm that the models present accurate predictions of the flow depths and velocities, while the MLP network outperforms ANFIS with a mean relative error percentage of 0.67%. New equations are also proposed for estimating the super-elevation and flow of the study.