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    Artificial Neural Network Model for Subsurface-Drained Farmlands

    Source: Journal of Irrigation and Drainage Engineering:;1997:;Volume ( 123 ):;issue: 004
    Author:
    Chun-Chieh Yang
    ,
    Shiv O. Prasher
    ,
    René Lacroix
    ,
    Sri Sreekanth
    ,
    Naveen K. Patni
    ,
    Lucie Masse
    DOI: 10.1061/(ASCE)0733-9437(1997)123:4(285)
    Publisher: American Society of Civil Engineers
    Abstract: This paper describes the development of an artificial neural network (ANN) model to simulate fluctuations in midspan water-table depths and drain outflows, as influenced by daily rainfall and potential evapotranspiration rates. Unlike conventional models, ANN models do not require explicit relationships between inputs and outputs. Instead, ANNs map the implicit relationship between inputs and outputs through training by field observations. Compared with conventional models, the ANN model requires fewer input parameters since the inputs that remain constant are not considered by ANNs. Therefore ANNs can be executed quickly on a microcomputer. These benefits can be exploited in the real-time control of water-table management systems. The model was developed using field observations of water-table depths from 1991 to 1993 and drain outflows from 1991 to 1994 made at an agricultural field in Ottawa, Canada. The root mean squared errors and standard deviation of errors of simulated results were found to range from 46.5 to 161.1 mm and 46.6 to 139.2 mm, respectively, thus showing potential applications of ANNs in land drainage engineering.
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      Artificial Neural Network Model for Subsurface-Drained Farmlands

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    http://yetl.yabesh.ir/yetl1/handle/yetl/27810
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    contributor authorChun-Chieh Yang
    contributor authorShiv O. Prasher
    contributor authorRené Lacroix
    contributor authorSri Sreekanth
    contributor authorNaveen K. Patni
    contributor authorLucie Masse
    date accessioned2017-05-08T20:48:47Z
    date available2017-05-08T20:48:47Z
    date copyrightJuly 1997
    date issued1997
    identifier other%28asce%290733-9437%281997%29123%3A4%28285%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/27810
    description abstractThis paper describes the development of an artificial neural network (ANN) model to simulate fluctuations in midspan water-table depths and drain outflows, as influenced by daily rainfall and potential evapotranspiration rates. Unlike conventional models, ANN models do not require explicit relationships between inputs and outputs. Instead, ANNs map the implicit relationship between inputs and outputs through training by field observations. Compared with conventional models, the ANN model requires fewer input parameters since the inputs that remain constant are not considered by ANNs. Therefore ANNs can be executed quickly on a microcomputer. These benefits can be exploited in the real-time control of water-table management systems. The model was developed using field observations of water-table depths from 1991 to 1993 and drain outflows from 1991 to 1994 made at an agricultural field in Ottawa, Canada. The root mean squared errors and standard deviation of errors of simulated results were found to range from 46.5 to 161.1 mm and 46.6 to 139.2 mm, respectively, thus showing potential applications of ANNs in land drainage engineering.
    publisherAmerican Society of Civil Engineers
    titleArtificial Neural Network Model for Subsurface-Drained Farmlands
    typeJournal Paper
    journal volume123
    journal issue4
    journal titleJournal of Irrigation and Drainage Engineering
    identifier doi10.1061/(ASCE)0733-9437(1997)123:4(285)
    treeJournal of Irrigation and Drainage Engineering:;1997:;Volume ( 123 ):;issue: 004
    contenttypeFulltext
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