Evaluation of Ensemble Machine Learning Techniques for Prediction of Oxygen Transfer in Self-Aerated Flows.
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| Title: | Evaluation of Ensemble Machine Learning Techniques for Prediction of Oxygen Transfer in Self-Aerated Flows. |
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| Authors: | Tiwari, Ashwini1 (AUTHOR) ashwitiwari@gmail.com, Prasad, K. S. Hari2 (AUTHOR) suryafce@iitr.ac.in, Ojha, C. S. P.2 (AUTHOR) c.ojha@ce.iitr.ac.in |
| Source: | Journal of Environmental Engineering. Jun2026, Vol. 152 Issue 6, p1-15. 15p. |
| Subjects: | Boosting algorithms, Aerated water flow, Random forest algorithms, Ensemble learning, Water quality, Oxygen, Hydraulic structures, Water aeration |
| Abstract: | Dissolved oxygen (DO) refers to the mass of oxygen that is contained in the water. The concentration of DO is an important indicator of the water quality. Maintaining adequate DO levels in surface waters is necessary to sustain public health, aquatic ecosystems, and agricultural water quality worldwide. Oxygen-deficient streams cause fish death, pathogen growth, and reduced self-purification capacity issues that are particularly severe in densely populated and agriculturally intensive regions. The potential contribution of hydraulic structures to increase oxygen content in rivers through air entrainment has been recognized in the past. Aeration efficiency has been the subject of several empirical relationships, which often do not yield very good results. One of the probable reasons may be the complexity of the flow field and associated turbulence, which are difficult to account for. Even if these effects are accounted for, their applications at other scales or other hydraulic conditions may be infeasible. An alternative to these models is missing in the literature. Through the use of machine learning (ML) algorithms, the present work explores the likely improvement over the use of traditionally used empirical models. In this study, ensemble ML models such as random forest (RF), gradient boosting (GB), extreme GB (XGB), and adaptive boosting (ADB) are used to predict E 20 for hydraulic structures. The ML models employed in this study use GridSearchCV for hyperparameter optimization along with K-fold cross-validation. A data set available in literature comprising a wide range of flow rates, tailwater depth, and head loss has been used in this study. To evaluate the performance of these models, several performance metrics, such as correlation coefficient (CC), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), mean absolute percentage of error (MAPE), Willmott's index of agreement (I A), and percent bias (PBIAS) are used. Results show that the GB algorithm is the most accurate among all the models, with a CC of 0.996 and 0.995 for the training and testing data sets, respectively. The best models (GB and RF) were compared with existing empirical equations, and it was observed that GB and RF outperformed in terms of accuracy and generalization. SHAP (Shapley additive explanations) is used to understand the influence that each input has on the model's output prediction. Sensitivity analysis using SHAP shows that flow rate is the most important feature. Uncertainty analysis in predicting aeration efficiency using the proposed models was conducted and results highlighted the GB model's robustness, showing the smallest uncertainty band, 0.095, compared to the other ML models, securing the first rank. Practical Applications: This study applies ensemble machine learning (ML) models to predict aeration efficiency in rivers and streams, focusing on real-world, field-scale conditions. Traditional empirical equations have been widely used, but they are valid for specific flow conditions and are not very generalized. The ML models developed here, which include gradient boosting and random forest, provide accurate and adaptable predictions based on diverse hydraulic parameters, including discharge, head loss, and tailwater depth. The approach is practical for engineers and practitioners involved in the design and retrofitting of weirs and dams, ensuring that oxygen transfer to water bodies is both effective and reliable. The study will also encourage further investigations into the efficacy of ML models for the analysis of self-aerated flows. By linking data-driven insights with real-world applications, the study supports sustainable water management practices and helps safeguard aquatic ecosystems under changing environmental conditions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
| Abstract: | Dissolved oxygen (DO) refers to the mass of oxygen that is contained in the water. The concentration of DO is an important indicator of the water quality. Maintaining adequate DO levels in surface waters is necessary to sustain public health, aquatic ecosystems, and agricultural water quality worldwide. Oxygen-deficient streams cause fish death, pathogen growth, and reduced self-purification capacity issues that are particularly severe in densely populated and agriculturally intensive regions. The potential contribution of hydraulic structures to increase oxygen content in rivers through air entrainment has been recognized in the past. Aeration efficiency has been the subject of several empirical relationships, which often do not yield very good results. One of the probable reasons may be the complexity of the flow field and associated turbulence, which are difficult to account for. Even if these effects are accounted for, their applications at other scales or other hydraulic conditions may be infeasible. An alternative to these models is missing in the literature. Through the use of machine learning (ML) algorithms, the present work explores the likely improvement over the use of traditionally used empirical models. In this study, ensemble ML models such as random forest (RF), gradient boosting (GB), extreme GB (XGB), and adaptive boosting (ADB) are used to predict E 20 for hydraulic structures. The ML models employed in this study use GridSearchCV for hyperparameter optimization along with K-fold cross-validation. A data set available in literature comprising a wide range of flow rates, tailwater depth, and head loss has been used in this study. To evaluate the performance of these models, several performance metrics, such as correlation coefficient (CC), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), mean absolute percentage of error (MAPE), Willmott's index of agreement (I A), and percent bias (PBIAS) are used. Results show that the GB algorithm is the most accurate among all the models, with a CC of 0.996 and 0.995 for the training and testing data sets, respectively. The best models (GB and RF) were compared with existing empirical equations, and it was observed that GB and RF outperformed in terms of accuracy and generalization. SHAP (Shapley additive explanations) is used to understand the influence that each input has on the model's output prediction. Sensitivity analysis using SHAP shows that flow rate is the most important feature. Uncertainty analysis in predicting aeration efficiency using the proposed models was conducted and results highlighted the GB model's robustness, showing the smallest uncertainty band, 0.095, compared to the other ML models, securing the first rank. Practical Applications: This study applies ensemble machine learning (ML) models to predict aeration efficiency in rivers and streams, focusing on real-world, field-scale conditions. Traditional empirical equations have been widely used, but they are valid for specific flow conditions and are not very generalized. The ML models developed here, which include gradient boosting and random forest, provide accurate and adaptable predictions based on diverse hydraulic parameters, including discharge, head loss, and tailwater depth. The approach is practical for engineers and practitioners involved in the design and retrofitting of weirs and dams, ensuring that oxygen transfer to water bodies is both effective and reliable. The study will also encourage further investigations into the efficacy of ML models for the analysis of self-aerated flows. By linking data-driven insights with real-world applications, the study supports sustainable water management practices and helps safeguard aquatic ecosystems under changing environmental conditions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 07339372 |
| DOI: | 10.1061/JOEEDU.EEENG-8415 |
