Novel application of robust GWO-KELM model in predicting discharge coefficient of radial gates: a field data-based analysis

Abstract

Accurate determination of discharge capacity in radial gates as commonly designed check structures is of great importance in hydraulic engineering research. The discharge coefficient plays the most dominant role in calculating the flow discharge through the radial gates. The main goal of this study is to adopt Grey Wolf Optimization-based Kernel Extreme Learning Machine (KELM-GWO) to further improve the prediction accuracy of the discharge coefficient of radial gates. To compare the supreme performance of the proposed model, kernel-depend support vector machine (SVM) and Gaussian process regression (GPR) were employed. An extensive field database consisting of 546 data samples gathered from different types of radial gates was established for building prediction models. The modeling results indicated that the proposed KELM-GWO model (correlation coefficient [R] = 0.927, and root mean squared error [RMSE] = 0.018) and SVM model (correlation coefficient [R] = 0.940, and root mean squared error [RMSE] = 0.022) demonstrated better performance under free and submerged flow conditions, respectively. Moreover, it was found that the applied kernel-depend approaches can be suitable options to predict the discharge coefficient of radial gates under varied submergence conditions with a satisfactory level of accuracy.

Optimization Strategy of Classification Model Based on Weighted Implicit Optimal Extreme Learning Machine

“…The research proved that the model had excellent performance compared with Support Vector Machine (SVM) and Gaussian Process Regression (GPR). However, there were still efficiency problems [18].Aiming at the ECG signal separation problem in the diagnosis of heart disease, A. Diker et al proposed the differential evolution algorithm combined with ELM's hidden neurons to solve the accuracy problem of ELM classification. The experimental results showed that the accuracy rate reached 83.12%, which still had a large optimization space [19].…”