Improving the performance of multi-layer perceptron and radial basis function models with a decision tree model to predict flow variables in a sharp 90° bend

Abstract

The use of artificial intelligence methods in different hydraulic sciences has become conventional in recent years. In this study, two artificial neural networks (ANN), namely multilayer perceptron (MLP) and radial basis function (RBF) models were modified with decision trees (DT) and designed as two new hybrid models, namely DT-MLP and DT-RBF. The performance of the proposed hybrid (DT-MLP and DT-RBF) and simple (MLP and RBF) models was compared for velocity and water surface prediction in sharp 90° bends. The experimental data for 5 different hydraulic conditions in a sharp 90° bend were used to train and test the models. In velocity prediction, the mean absolute error (MAE), root mean square error (RMSE) and relative error (δ) values decreased with the DT-MLP model compared to the MLP model by 16%, 9% and 0.17% and the values of DT-RBF reduced by 11%, 7% and 0.11% compared to the RBF model, respectively. For water surface prediction, the MAE, RMSE and δ errors decreased with the DT-MLP model by 5.2%, 5.5% and 0.095% compared with MLP and with the DT-RBF model the errors decreased by 20%, 23% and 0.5% compared with RBF, respectively. Using the new proposed hybrid algorithms based on decision trees enhanced the simple MLP and RBF models’ performance.