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 investiga...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-12-01
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| Series: | Applied Water Science |
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| Online Access: | https://doi.org/10.1007/s13201-022-01841-x |
| _version_ | 1797973590154412032 |
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| author | Aliasghar Azma Mohammad Tavakol Sadrabadi Yakun Liu Masoumeh Azma Di Zhang Ze Cao Zhuoyue Li |
| author_facet | Aliasghar Azma Mohammad Tavakol Sadrabadi Yakun Liu Masoumeh Azma Di Zhang Ze Cao Zhuoyue Li |
| author_sort | Aliasghar Azma |
| collection | DOAJ |
| description | 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 (Q in /Q t ) with R 2 = 0.982, while both XGBoost and CatBoost models have shown close performance in terms of estimating the discharge coefficient (C d ) with R 2 of CatBoost equal to 0.994 and R 2 of XGBoost equal to 0.992. Weakest results were also produced by Decision tree regressor with R 2 = 0.821 and 0.865 for estimation of C d and Qin/Qt values. |
| first_indexed | 2024-04-11T04:05:34Z |
| format | Article |
| id | doaj.art-be876c7485d14f3d83cd39e5ddfc7c4a |
| institution | Directory Open Access Journal |
| issn | 2190-5487 2190-5495 |
| language | English |
| last_indexed | 2024-04-11T04:05:34Z |
| publishDate | 2022-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Water Science |
| spelling | doaj.art-be876c7485d14f3d83cd39e5ddfc7c4a2023-01-01T12:24:38ZengSpringerOpenApplied Water Science2190-54872190-54952022-12-0113211610.1007/s13201-022-01841-xBoosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirsAliasghar Azma0Mohammad Tavakol Sadrabadi1Yakun Liu2Masoumeh Azma3Di Zhang4Ze Cao5Zhuoyue Li6School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of TechnologyAutonomous Vehicles and Artificial Intelligence Laboratory (AVAILab), Centre for Future Transport and Cities, Coventry UniversitySchool of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of TechnologySchool of Foreign Languages, University Lecturer, Nanjing Xiaozhuang UniversitySchool of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of TechnologySchool of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of TechnologySchool of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of TechnologyAbstract 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 (Q in /Q t ) with R 2 = 0.982, while both XGBoost and CatBoost models have shown close performance in terms of estimating the discharge coefficient (C d ) with R 2 of CatBoost equal to 0.994 and R 2 of XGBoost equal to 0.992. Weakest results were also produced by Decision tree regressor with R 2 = 0.821 and 0.865 for estimation of C d and Qin/Qt values.https://doi.org/10.1007/s13201-022-01841-xGabion weirsDischarge coefficientXGBOOstCatBoost |
| spellingShingle | Aliasghar Azma Mohammad Tavakol Sadrabadi Yakun Liu Masoumeh Azma Di Zhang Ze Cao Zhuoyue Li Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs Applied Water Science Gabion weirs Discharge coefficient XGBOOst CatBoost |
| title | Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs |
| title_full | Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs |
| title_fullStr | Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs |
| title_full_unstemmed | Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs |
| title_short | Boosting ensembles for estimation of discharge coefficient and through flow discharge in broad-crested gabion weirs |
| title_sort | boosting ensembles for estimation of discharge coefficient and through flow discharge in broad crested gabion weirs |
| topic | Gabion weirs Discharge coefficient XGBOOst CatBoost |
| url | https://doi.org/10.1007/s13201-022-01841-x |
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