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    Application of Hybrid AI Models for Accurate Prediction of Scour Depths under Submerged Circular Vertical Jet

    Source: Journal of Hydrologic Engineering:;2024:;Volume ( 029 ):;issue: 003::page 04024010-1
    Author:
    Sai Guguloth
    ,
    Manish Pandey
    ,
    Manali Pal
    DOI: 10.1061/JHYEFF.HEENG-6149
    Publisher: ASCE
    Abstract: This study utilized hybrid artificial intelligence (AI) models, created by integrating the extreme gradient boosting model with particle swarm optimization (XGBoost-PSO) and differential evolution (XGBoost-DE) algorithms. These models are applied to predict scour depths for two conditions, i.e., static (jet is turned off) and dynamic (jet is operational), that are formed due to submerged circular vertical jets. To assess the model’s effectiveness, a comparative analysis is conducted among individual AI models, including support vector regression, adaptive neuro-fuzzy inference system, and traditional regression methods, such as multiple linear regression and multiple nonlinear regression. The results from the cross-validation analysis reveal that the XGBoost-DE and XGBoost-PSO models showcase the highest performance metrics. In the testing dataset, both models achieve a high coefficient of determination (R2) of 0.93 for static scour depth prediction. For dynamic scour depth, the XGBoost-DE model achieves an R2 value of 0.88, while the XGBoost-PSO model achieves an R2 value of 0.91, confirming their predictive capabilities. The XGBoost-DE model exhibited low values of root mean square error (RMSE) and mean absolute error (MAE) at 0.199 and 0.09, respectively, in static scour depth prediction. Similarly, the XGBoost-PSO model posted RMSE and MAE values of 0.227 and 0.159 for dynamic scour depths. In a nutshell, the significance of the study includes: (1) the utilization of first-hand hybridized AI models to predict the scour depths under submerged circular vertical jets and (2) the demonstrated superiority of these hybrid models over existing methods. These advancements provide valuable support to civil engineers in accurately estimating scour depths under submerged circular vertical jets.
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      Application of Hybrid AI Models for Accurate Prediction of Scour Depths under Submerged Circular Vertical Jet

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4297702
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    contributor authorSai Guguloth
    contributor authorManish Pandey
    contributor authorManali Pal
    date accessioned2024-04-27T22:52:05Z
    date available2024-04-27T22:52:05Z
    date issued2024/06/01
    identifier other10.1061-JHYEFF.HEENG-6149.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4297702
    description abstractThis study utilized hybrid artificial intelligence (AI) models, created by integrating the extreme gradient boosting model with particle swarm optimization (XGBoost-PSO) and differential evolution (XGBoost-DE) algorithms. These models are applied to predict scour depths for two conditions, i.e., static (jet is turned off) and dynamic (jet is operational), that are formed due to submerged circular vertical jets. To assess the model’s effectiveness, a comparative analysis is conducted among individual AI models, including support vector regression, adaptive neuro-fuzzy inference system, and traditional regression methods, such as multiple linear regression and multiple nonlinear regression. The results from the cross-validation analysis reveal that the XGBoost-DE and XGBoost-PSO models showcase the highest performance metrics. In the testing dataset, both models achieve a high coefficient of determination (R2) of 0.93 for static scour depth prediction. For dynamic scour depth, the XGBoost-DE model achieves an R2 value of 0.88, while the XGBoost-PSO model achieves an R2 value of 0.91, confirming their predictive capabilities. The XGBoost-DE model exhibited low values of root mean square error (RMSE) and mean absolute error (MAE) at 0.199 and 0.09, respectively, in static scour depth prediction. Similarly, the XGBoost-PSO model posted RMSE and MAE values of 0.227 and 0.159 for dynamic scour depths. In a nutshell, the significance of the study includes: (1) the utilization of first-hand hybridized AI models to predict the scour depths under submerged circular vertical jets and (2) the demonstrated superiority of these hybrid models over existing methods. These advancements provide valuable support to civil engineers in accurately estimating scour depths under submerged circular vertical jets.
    publisherASCE
    titleApplication of Hybrid AI Models for Accurate Prediction of Scour Depths under Submerged Circular Vertical Jet
    typeJournal Article
    journal volume29
    journal issue3
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/JHYEFF.HEENG-6149
    journal fristpage04024010-1
    journal lastpage04024010-17
    page17
    treeJournal of Hydrologic Engineering:;2024:;Volume ( 029 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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