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    Evaluation of Machine-Learning Approaches in the Automation of Irrigation Canals Using a Variable-Height Weir

    Source: Journal of Irrigation and Drainage Engineering:;2024:;Volume ( 150 ):;issue: 006::page 04024030-1
    Author:
    Shahla Zamani
    ,
    Atefeh Parvaresh Rizi
    ,
    Salah Kouchakzadeh
    ,
    Hedieh Sajedi
    DOI: 10.1061/JIDEDH.IRENG-10327
    Publisher: American Society of Civil Engineers
    Abstract: Automatic check structures can be important for water distribution in irrigation networks. In this research, a control algorithm was developed for a variable height whirling (VHW) weir, as a regulating structure equipped with a control mechanism. A local feedback controller was established for adjusting the flow depth upstream of the weir within a marginal target range. The control performance of the VHW weir was investigated using two methods: (1) K nearest neighbor (KNN); and (2) artificial neural network (ANN). The required data for methods were compiled in a long trapezoidal canal using different water depth targets. The inputs consisted of the discharge at the canal entrance, the variation of the discharge in three sequential periods, the water level deviation from the target value, and the offtake discharge. The model output was the set point of the instantaneous weir angle value, which represents the crest weir height, for maintaining the water depth within the target range. Different statistical indicators were employed to investigate the control performance. The results indicated that the ANN models, which were applied to cases with and without offtake in operation, provided 0.95 and 0.93 correlation coefficients, respectively. Also, the proposed neural model performed slightly better than the KNN algorithm, which yielded marginally higher error in output predictions.
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      Evaluation of Machine-Learning Approaches in the Automation of Irrigation Canals Using a Variable-Height Weir

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4304527
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    contributor authorShahla Zamani
    contributor authorAtefeh Parvaresh Rizi
    contributor authorSalah Kouchakzadeh
    contributor authorHedieh Sajedi
    date accessioned2025-04-20T10:20:50Z
    date available2025-04-20T10:20:50Z
    date copyright9/28/2024 12:00:00 AM
    date issued2024
    identifier otherJIDEDH.IRENG-10327.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4304527
    description abstractAutomatic check structures can be important for water distribution in irrigation networks. In this research, a control algorithm was developed for a variable height whirling (VHW) weir, as a regulating structure equipped with a control mechanism. A local feedback controller was established for adjusting the flow depth upstream of the weir within a marginal target range. The control performance of the VHW weir was investigated using two methods: (1) K nearest neighbor (KNN); and (2) artificial neural network (ANN). The required data for methods were compiled in a long trapezoidal canal using different water depth targets. The inputs consisted of the discharge at the canal entrance, the variation of the discharge in three sequential periods, the water level deviation from the target value, and the offtake discharge. The model output was the set point of the instantaneous weir angle value, which represents the crest weir height, for maintaining the water depth within the target range. Different statistical indicators were employed to investigate the control performance. The results indicated that the ANN models, which were applied to cases with and without offtake in operation, provided 0.95 and 0.93 correlation coefficients, respectively. Also, the proposed neural model performed slightly better than the KNN algorithm, which yielded marginally higher error in output predictions.
    publisherAmerican Society of Civil Engineers
    titleEvaluation of Machine-Learning Approaches in the Automation of Irrigation Canals Using a Variable-Height Weir
    typeJournal Article
    journal volume150
    journal issue6
    journal titleJournal of Irrigation and Drainage Engineering
    identifier doi10.1061/JIDEDH.IRENG-10327
    journal fristpage04024030-1
    journal lastpage04024030-10
    page10
    treeJournal of Irrigation and Drainage Engineering:;2024:;Volume ( 150 ):;issue: 006
    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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