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    Determination of Input for Artificial Neural Networks for Flood Forecasting Using the Copula Entropy Method

    Source: Journal of Hydrologic Engineering:;2014:;Volume ( 019 ):;issue: 011
    Author:
    Lu Chen
    ,
    Lei Ye
    ,
    Vijay Singh
    ,
    Jianzhong Zhou
    ,
    Shenglian Guo
    DOI: 10.1061/(ASCE)HE.1943-5584.0000932
    Publisher: American Society of Civil Engineers
    Abstract: Artificial neural networks (ANNs) have proved to be an efficient alternative to traditional methods for hydrological modeling. One of the most important steps in the ANN development is the determination of significant input variables. This study proposes a new method based on the copula-entropy (CE) theory to identify the inputs of an ANN model. The CE theory permits to calculate mutual information (MI) and partial mutual information (PMI), which characterizes the dependence between potential model input and output variables directly instead of calculating the marginal and joint probability distributions. Two tests were carried out for verifying the accuracy and performance of the CE method. The CE theory-based input determination methodology was applied to identify suitable inputs for a flood forecasting model for a real-world case study involving the three gorges reservoir (TGR) in China. Test results of application of the flood forecasting model to the upper Yangtze River indicates that the proposed method appropriately identifies inputs for the ANN with the smallest root-mean-square error (RMSE) for training, testing, and validation data.
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      Determination of Input for Artificial Neural Networks for Flood Forecasting Using the Copula Entropy Method

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    http://yetl.yabesh.ir/yetl1/handle/yetl/63811
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    • Journal of Hydrologic Engineering

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    contributor authorLu Chen
    contributor authorLei Ye
    contributor authorVijay Singh
    contributor authorJianzhong Zhou
    contributor authorShenglian Guo
    date accessioned2017-05-08T21:50:26Z
    date available2017-05-08T21:50:26Z
    date copyrightNovember 2014
    date issued2014
    identifier other%28asce%29hy%2E1943-7900%2E0000004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/63811
    description abstractArtificial neural networks (ANNs) have proved to be an efficient alternative to traditional methods for hydrological modeling. One of the most important steps in the ANN development is the determination of significant input variables. This study proposes a new method based on the copula-entropy (CE) theory to identify the inputs of an ANN model. The CE theory permits to calculate mutual information (MI) and partial mutual information (PMI), which characterizes the dependence between potential model input and output variables directly instead of calculating the marginal and joint probability distributions. Two tests were carried out for verifying the accuracy and performance of the CE method. The CE theory-based input determination methodology was applied to identify suitable inputs for a flood forecasting model for a real-world case study involving the three gorges reservoir (TGR) in China. Test results of application of the flood forecasting model to the upper Yangtze River indicates that the proposed method appropriately identifies inputs for the ANN with the smallest root-mean-square error (RMSE) for training, testing, and validation data.
    publisherAmerican Society of Civil Engineers
    titleDetermination of Input for Artificial Neural Networks for Flood Forecasting Using the Copula Entropy Method
    typeJournal Paper
    journal volume19
    journal issue11
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/(ASCE)HE.1943-5584.0000932
    treeJournal of Hydrologic Engineering:;2014:;Volume ( 019 ):;issue: 011
    contenttypeFulltext
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