Assessing the Capability of KELM Meta-Model Approach in Predicting the Energy Dissipation in Different Shapes Channels

Abstract

For transition of a supercritical flow into a subcritical flow in an open channel, a hydraulic jump phenomenon is used. Different shaped channels are used as useful tools in the extra energy dissipation of the hydraulic jump. Accurate prediction of relative energy dissipation is important in designing hydraulic structures. The aim of this paper is to assess the capability of a Kernel extreme Learning Machine (KELM) meta-model approach in predicting the energy dissipation in different shaped channels (i.e., rectangular and trapezoidal channels). Different experimental data series were used to develop the models. The obtained results approved the capability of the KELM model in predicting the energy dissipation. Results showed that the rectangular channel led to better outcomes. Based on the results obtained for the rectangular and trapezoidal channels, the combination of Fr1, (y2-y1)/y1, and W/Z parameters performed more successfully. Also, comparison between KELM and the Artificial Neural Networks (ANN) approach showed that KELM is more successful in the predicting process.