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    Evaluation of Ensemble Machine Learning Techniques for Prediction of Oxygen Transfer in Self-Aerated Flows

    Source: Journal of Environmental Engineering:;2026:;Volume ( 152 ):;issue: 006::page 04026017-1
    Author:
    Tiwari, Ashwini
    ,
    Prasad, K. S. Hari
    ,
    Ojha, C. S. P.
    DOI: 10.1061/JOEEDU.EEENG-8415
    Publisher: American Society of Civil Engineers
    Abstract: AbstractDissolved 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, ...Practical ApplicationsThis 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 ...
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      Evaluation of Ensemble Machine Learning Techniques for Prediction of Oxygen Transfer in Self-Aerated Flows

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4312867
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    • Journal of Environmental Engineering

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    contributor authorTiwari, Ashwini
    contributor authorPrasad, K. S. Hari
    contributor authorOjha, C. S. P.
    date accessioned2026-08-20T11:56:10Z
    date available2026-08-20T11:56:10Z
    date copyright2026/03/20
    date issued2026
    identifier otherJOEEDU.EEENG-8415.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4312867
    description abstractAbstractDissolved 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, ...Practical ApplicationsThis 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 ...
    publisherAmerican Society of Civil Engineers
    titleEvaluation of Ensemble Machine Learning Techniques for Prediction of Oxygen Transfer in Self-Aerated Flows
    typeJournal Article
    journal volume152
    journal issue6
    journal titleJournal of Environmental Engineering
    identifier doi10.1061/JOEEDU.EEENG-8415
    journal fristpage04026017-1
    journal lastpage04026017-15
    page15
    treeJournal of Environmental Engineering:;2026:;Volume ( 152 ):;issue: 006
    contenttypeFulltext
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