Iranian Journal of Irrigation & Drainage

Iranian Journal of Irrigation & Drainage

Hybrid learning-based estimation of discharge coefficient in triangular and trapezoidal side weirs using SVM optimized by HOA and RSA

Document Type : Original Article

Authors
Kiyoumars Roushangar 1
Aydin Panahi 2
1 Department Hydraulic Engineering, Faculty of Civil Engineering, Tabriz University, Iran
2 Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
10.22034/idj.2025.537571.2616
Abstract
Side weirs play a pivotal role in flow regulation and water measurement, serving as essential components in flood management systems, urban runoff collection, and irrigation and drainage networks. Accurate prediction of the discharge coefficient is critical for the optimal design of these structures. In this study, to enhance the modeling accuracy of the discharge coefficient in sharp-crested trapezoidal, broad-crested trapezoidal, and sharp-crested triangular side weirs, Support Vector Machine (SVM) models were hybridized with two metaheuristic algorithms: Horse Optimization Algorithm (HOA) and Reptile Search Algorithm (RSA). After identifying the key dimensionless variables, multiple models were developed for each weir geometry. The results indicate that both hybrid models exhibit strong predictive capabilities; however, SVM-HOA consistently outperformed SVM-RSA in the testing phase. Specifically, for sharp-crested trapezoidal, broad-crested trapezoidal, and sharp-crested triangular weirs, the SVM-HOA model achieved NSE, RMSE, and R values of (0.913, 0.019, 0.959), (0.933, 0.014, 0.966), and (0.932, 0.015, 0.967), respectively, demonstrating its superior performance across all geometries.
Keywords
Discharge coefficient
Machine learning
Meta-heuristic algorithms
Sensitivity analysis
Side weir
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Iranian Journal of Irrigation & Drainage
Volume 19, Issue 6 - Serial Number 115
March and April 2026
Pages 885-897