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    Application of a Combination Model Based on Wavelet Transform and KPLS-ARMA for Urban Annual Water Demand Forecasting

    Source: Journal of Water Resources Planning and Management:;2014:;Volume ( 140 ):;issue: 008
    Author:
    Lili Huang
    ,
    Chi Zhang
    ,
    Yong Peng
    ,
    Huicheng Zhou
    DOI: 10.1061/(ASCE)WR.1943-5452.0000397
    Publisher: American Society of Civil Engineers
    Abstract: A combination of models including wavelet transform and kernel partial least squares-autoregressive moving average (KPLS-ARMA) is proposed to explore the nonstationarity of the urban annual water demand series, the nonlinear relationships between water demand series and its determinants, and the high correlations among those determinants, based on which a novel forecast model is proposed for urban annual water demand. First, by Mallat algorithm, a nonstationary urban annual water demand series is decomposed and reconstructed into one low-frequency component and one or several high-frequency components. Following that, the kernel partial least squares (KPLS) model is applied to simulating the low-frequency component. An autoregressive moving average (ARMA) model is constructed for each of the high-frequency components. The combined models are applied to understanding the nonstationarity and forecasting the annual water demand of Dalian City. The results are then compared with those from other several methods. It is shown that the proposed method, which combines advanced statistical tools (such as wavelet transform and artificial intelligence) and traditional statistical models, provides the most accurate forecast of urban annual water demand in the city.
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      Application of a Combination Model Based on Wavelet Transform and KPLS-ARMA for Urban Annual Water Demand Forecasting

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    http://yetl.yabesh.ir/yetl1/handle/yetl/70257
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    contributor authorLili Huang
    contributor authorChi Zhang
    contributor authorYong Peng
    contributor authorHuicheng Zhou
    date accessioned2017-05-08T22:03:54Z
    date available2017-05-08T22:03:54Z
    date copyrightAugust 2014
    date issued2014
    identifier other%28asce%29ww%2E1943-5460%2E0000030.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/70257
    description abstractA combination of models including wavelet transform and kernel partial least squares-autoregressive moving average (KPLS-ARMA) is proposed to explore the nonstationarity of the urban annual water demand series, the nonlinear relationships between water demand series and its determinants, and the high correlations among those determinants, based on which a novel forecast model is proposed for urban annual water demand. First, by Mallat algorithm, a nonstationary urban annual water demand series is decomposed and reconstructed into one low-frequency component and one or several high-frequency components. Following that, the kernel partial least squares (KPLS) model is applied to simulating the low-frequency component. An autoregressive moving average (ARMA) model is constructed for each of the high-frequency components. The combined models are applied to understanding the nonstationarity and forecasting the annual water demand of Dalian City. The results are then compared with those from other several methods. It is shown that the proposed method, which combines advanced statistical tools (such as wavelet transform and artificial intelligence) and traditional statistical models, provides the most accurate forecast of urban annual water demand in the city.
    publisherAmerican Society of Civil Engineers
    titleApplication of a Combination Model Based on Wavelet Transform and KPLS-ARMA for Urban Annual Water Demand Forecasting
    typeJournal Paper
    journal volume140
    journal issue8
    journal titleJournal of Water Resources Planning and Management
    identifier doi10.1061/(ASCE)WR.1943-5452.0000397
    treeJournal of Water Resources Planning and Management:;2014:;Volume ( 140 ):;issue: 008
    contenttypeFulltext
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