Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs

Aliasghar Azma; Mohammad Tavakol Sadrabadi; Yakun Liu; Masoumeh Azma; Di Zhang; Ze Cao; Zhuoyue Li

doi:10.1007/s13201-022-01841-x

Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs

Aliasghar Azma, Mohammad Tavakol Sadrabadi, Yakun Liu, Masoumeh Azma, Di Zhang, Ze Cao, Zhuoyue Li

Centre for Future Transport and Cities

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Abstract

Gabion weirs are environment-friendly structures widely used for irrigation and drainage network purposes. These structures' hydraulic performance is fundamentally different from solid weirs' due to their porosity and the existence of a through-flow discharge. This paper investigates the reliability and suitability of a number of Machine learning models for estimation of hydraulic performance of gabion weirs. Generally, three different Boosting ensemble models, including Gradient Boosting, XGBoost, and CatBoost, are compared to the well-known Random Forest and a Stacked Regression model, with respect to their accuracy in prediction of the discharge coefficient and through-flow discharge ratio of gabion weirs in free flow conditions. The Bayesian optimization approach is used to fine-tune model hyper-parameters automatically. Recursive feature elimination analysis is also performed to find optimum combination of features for each model. Results indicate that the CatBoost model has outperformed other models in terms of estimating the through flow discharge ratio (Qin/Qt) with R2 = 0.982, while both XGBoost and CatBoost models have shown close performance in terms of estimating the discharge coefficient (Cd) with R2 of CatBoost equal to 0.994 and R2 of XGBoost equal to 0.992. Weakest results were also produced by Decision tree regressor with R2 = 0.821 and 0.865 for estimation of Cd and Qin/Qt values.

Original language	English
Article number	45
Number of pages	16
Journal	Applied Water Science
Volume	13
Issue number	2
Early online date	30 Dec 2022
DOIs	https://doi.org/10.1007/s13201-022-01841-x
Publication status	Published - Feb 2023

Bibliographical note

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Funder

This research report was financially supported by the National Natural Science Foundation of China (No. 51578163), Guangxi Science and Technology Base and Talent Special Project (AD21075031), Guangxi Key R&D Project (AB21220012), Central Funding Project for Local Science and Technology Development (ZY21195010), Special fund project for Bagui scholars [(2019) No. 79], Nanning Key R&D Project (20223024).

Publisher Copyright:
© 2022, The Author(s).

Keywords

Gabion weirs
Discharge coefficient
XGBOOst
CatBoost

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Cite this

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title = "Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs",

abstract = "Gabion weirs are environment-friendly structures widely used for irrigation and drainage network purposes. These structures' hydraulic performance is fundamentally different from solid weirs' due to their porosity and the existence of a through-flow discharge. This paper investigates the reliability and suitability of a number of Machine learning models for estimation of hydraulic performance of gabion weirs. Generally, three different Boosting ensemble models, including Gradient Boosting, XGBoost, and CatBoost, are compared to the well-known Random Forest and a Stacked Regression model, with respect to their accuracy in prediction of the discharge coefficient and through-flow discharge ratio of gabion weirs in free flow conditions. The Bayesian optimization approach is used to fine-tune model hyper-parameters automatically. Recursive feature elimination analysis is also performed to find optimum combination of features for each model. Results indicate that the CatBoost model has outperformed other models in terms of estimating the through flow discharge ratio (Qin/Qt) with R2 = 0.982, while both XGBoost and CatBoost models have shown close performance in terms of estimating the discharge coefficient (Cd) with R2 of CatBoost equal to 0.994 and R2 of XGBoost equal to 0.992. Weakest results were also produced by Decision tree regressor with R2 = 0.821 and 0.865 for estimation of Cd and Qin/Qt values.",

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AU - Azma, Masoumeh

AU - Zhang, Di

AU - Cao, Ze

AU - Li, Zhuoyue

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Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs

Abstract

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