Rotary gate discharge determination for inclusive data from free to submerged flow conditions using ENN, ENN–GA, and SVM–SA

Abstract

This study aims at evaluating the performance of the Elman Neural Network (ENN), Elman Neural Network-Genetic Algorithm (ENN–GA), and Support Vector Machine-simulated annealing (SVM–SA) in determining the discharge of a newly proposed rotary gate for the inclusive data range from free flow to highly submerged conditions. For individual free and submerged flows, the models performed as well as that of the traditional relationships. However, the superiority of the intelligent models comes when dealing with the inclusive data set of both flow conditions, where no deterministic approach is available for discharge evaluation prior to specifying the threshold condition. In such complex flow conditions, the ENN–GA hybrid model with a proper structure determined the discharge with rather a high accuracy, i.e., SE of 6.12%. Also, in defining the threshold state, the ENN and ENN–GA models achieved superior results compared to the currently available relationship, i.e., it accurately recognized the threshold condition in almost 100% of the cases while the traditional relationship results were limited to 93% of the cases. Such accuracy of the employed model in assessing the discharge of the structure and its high ability in recognizing the flow state could be of great advantage for irrigation network structure automation. HIGHLIGHTS Application of the ENN, ENN-GA, SVM-SA to determine the flow parameters of the new rotary gate in free, submerged flow conditions, and more importantly the inclusive data from free to highly submerged state which was examined for the first time in this research.; To recognize the complex threshold flow between free and submerged conditions and the proposed Artificial Intelligence models with more than 98% accuracy which is crucial in field conditions and for gate automation.;

Keywords

• enn
• enn–ga
• flow condition
• rotary gate
• semicircular canal
• svm–sa