AccScience Publishing / AJWEP / Online First / DOI: 10.36922/AJWEP025120081
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ORIGINAL RESEARCH ARTICLE

Machine learning-based discharge coefficient estimation in trapezoidal-arched labyrinth weirs

Mohammad Heidarnejad1* ,  Jamal Feili2 ,  Mehdi Fuladipanah3 ,  Upaka Rathnayake4*
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1 Department of Water Science Engineering, Ahv. C., Islamic Azad University, Ahvaz, Iran
2 Khuzestan Water and Power Organization, Ahvaz, Khuzestan, Iran
3 Department of Civil Engineering, Ramh. C., Islamic Azad University, Ramhormoz, Iran
4 Department of Civil Engineering and Construction, Faculty of Engineering and Design, Atlantic Technological University, Sligo, Connacht, Ireland
AJWEP, 025120081 https://doi.org/10.36922/AJWEP025120081
Received: 17 March 2025 | Revised: 8 July 2025 | Accepted: 9 July 2025 | Published online: 13 August 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Weirs represent a frequently employed mechanism for regulating water surface elevations and managing flow within canals and hydraulic infrastructures. Among these, labyrinth weirs constitute a distinctive variant capable of accommodating a specific discharge while maintaining a reduced upstream water level compared to conventional linear weirs. The present investigation delved into the evaluation of the effectiveness of multilayer perceptron (MLP) networks, support vector machine (SVM), gene expression programming (GEP), and multivariate adaptive regression splines (MARS), aiming to predict the discharge coefficient (Cd) of a trapezoidal-arched labyrinth weir with an expanded central cycle. A dataset including 108 laboratory observations was utilized. The dimensionless parameters were obtained from the parameters including inside apex width of the middle cycle (w1), inside apex width of the end cycles (w2), weir height on the upstream side (B), unsubmerged total upstream head on the weir (Hd), and gravitational acceleration (g). The model was developed with the dimensionless parameters and Cd. Root mean square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and developed discrepancy ratio (DDR) were used as performance assessment criteria. Based on these metrics, all four models exhibited the latent capacity to predict the Cd value. However, the MLP model demonstrated superior performance among the models during both training (RMSE = 0.024, MAE = 0.020, R2 = 0.816, and Cd[DDRmax] = 8.07) and testing (RMSE = 0.011, MAE = 0.006, R2 = 0.688, and Cd[DDRmax] = 11.32) phases. Sequentially, the subsequent standings were secured by the SVM, GEP, and MARS. MLP outperformed SVM, GEP, and MARS models in predicting Cd, achieving the highest R² and lowest RMSE/MAE values.

Keywords
Discharge coefficient
Laboratory observations
Machine learning models
Prediction
Trapezoidal-arched labyrinth weir
Funding
None.
Conflict of interest
Upaka Rathnayake is an Editorial Board Member of this journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
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Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing
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