Novel application of robust GWO-KELM model in predicting discharge coefficient of radial gates: a field data-based analysis. Issue 2 (3rd February 2023)

• Record Type: Journal Article
• Authors: Roushangar, Kiyoumars; Alirezazadeh Sadaghiani, Arman; Shahnazi, Saman
• Is Part Of: Journal of hydroinformatics. Volume 25:Issue 2(2023)
• Page Start: 275
• Page End: 299
• Publication Date: 2023-02-03
• Subjects: discharge coefficient -- Gaussian process regression -- grey wolf optimization -- Kernel extreme learning machine -- radial gates -- support vector machine; Hydrology -- Data processing -- Periodicals; Geographic information systems -- Periodicals; Geographic information systems; Hydrology -- Data processing; Electronic journals; Periodicals; 551.480285
• DOI: 10.2166/hydro.2023.096
• Languages: English
• ISSNs: 1464-7141

Abstract

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. HIGHLIGHTS: Grey Wolf Optimization (GWO) and Kernel Extreme Learning Machine (KELM) were introduced for discharge coefficient prediction of radial gates. Support Vector Machine (SVM) and Gaussian Process Regression (GPR) were employed for comparative purposes. 546 filed data points from different types of radial gates used to feed the utilized models. Best input combinations for both free and submerged flow conditions were discussed.