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    Multiscale Characterization and Prediction of Reservoir Inflows Using MEMD-SLR Coupled Approach

    Source: Journal of Hydrologic Engineering:;2019:;Volume ( 024 ):;issue: 001
    Author:
    S. Adarsh; M. Janga Reddy
    DOI: 10.1061/(ASCE)HE.1943-5584.0001732
    Publisher: American Society of Civil Engineers
    Abstract: In this study, multiscale characterization of hydroclimatic time series was performed using the Hilbert-Huang Transform (HHT) approach. First, to investigate the possible teleconnections of monthly inflows into Hirakud reservoir in India, and two potential large-scale climate oscillations, El Niño Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO), the corresponding time series were decomposed using the multivariate empirical mode decomposition (MEMD) method, and then running correlation analysis, namely time-dependent intrinsic correlation (TDIC) analysis, was applied. TDIC analysis showed that there exists a long-range negative correlation between EQUINOO and reservoir inflow in most of the time scales, whereas a positive relation prevails between ENSO and inflow at the biannual time scale in particular. TDIC analysis further proved that the association between large-scale climate oscillations and reservoir inflow is not always unique but associated with localized reversals in the nature of correlation in the time domain; also, both the nature and strength of the association vary with time scales. Stemming from this finding, this paper proposes an innovative approach that combines MEMD and stepwise linear regression (SLR) methods for prediction of reservoir inflows. In this approach, the different modes obtained through MEMD are predicted independently by SLR fitting, considering the statistically significant inputs at respective time scales, and the final summation of the predicted modes gives the monthly inflows. A statistical performance evaluation based on multiple criteria showed that the proposed MEMD-SLR approach displayed better performance, with a high R2 of 0.905 and low values of root mean square error (RMSE) and bias for validation data over the EMD-SLR, M5 model tree, and multiple linear regression models for inflow prediction, including significant improvement in prediction of high inflows.
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      Multiscale Characterization and Prediction of Reservoir Inflows Using MEMD-SLR Coupled Approach

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    contributor authorS. Adarsh; M. Janga Reddy
    date accessioned2019-03-10T12:11:23Z
    date available2019-03-10T12:11:23Z
    date issued2019
    identifier other%28ASCE%29HE.1943-5584.0001732.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4255051
    description abstractIn this study, multiscale characterization of hydroclimatic time series was performed using the Hilbert-Huang Transform (HHT) approach. First, to investigate the possible teleconnections of monthly inflows into Hirakud reservoir in India, and two potential large-scale climate oscillations, El Niño Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO), the corresponding time series were decomposed using the multivariate empirical mode decomposition (MEMD) method, and then running correlation analysis, namely time-dependent intrinsic correlation (TDIC) analysis, was applied. TDIC analysis showed that there exists a long-range negative correlation between EQUINOO and reservoir inflow in most of the time scales, whereas a positive relation prevails between ENSO and inflow at the biannual time scale in particular. TDIC analysis further proved that the association between large-scale climate oscillations and reservoir inflow is not always unique but associated with localized reversals in the nature of correlation in the time domain; also, both the nature and strength of the association vary with time scales. Stemming from this finding, this paper proposes an innovative approach that combines MEMD and stepwise linear regression (SLR) methods for prediction of reservoir inflows. In this approach, the different modes obtained through MEMD are predicted independently by SLR fitting, considering the statistically significant inputs at respective time scales, and the final summation of the predicted modes gives the monthly inflows. A statistical performance evaluation based on multiple criteria showed that the proposed MEMD-SLR approach displayed better performance, with a high R2 of 0.905 and low values of root mean square error (RMSE) and bias for validation data over the EMD-SLR, M5 model tree, and multiple linear regression models for inflow prediction, including significant improvement in prediction of high inflows.
    publisherAmerican Society of Civil Engineers
    titleMultiscale Characterization and Prediction of Reservoir Inflows Using MEMD-SLR Coupled Approach
    typeJournal Paper
    journal volume24
    journal issue1
    journal titleJournal of Hydrologic Engineering
    identifier doi10.1061/(ASCE)HE.1943-5584.0001732
    page04018059
    treeJournal of Hydrologic Engineering:;2019:;Volume ( 024 ):;issue: 001
    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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