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An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction

1
Department of Civil Engineering, Graduate University of Advanced Technology-Kerman, P.O. Box 76315-116, Kerman, Iran
2
Department of Civil and Environmental Engineering, University of South Carolina, Columbia, SC 29208, USA
3
Department of Civil Engineering, Monash University, 23 College Walk, Clayton, VIC 3800, Australia
*
Authors to whom correspondence should be addressed.
Water 2019, 11(4), 709; https://doi.org/10.3390/w11040709
Received: 20 February 2019 / Revised: 29 March 2019 / Accepted: 2 April 2019 / Published: 6 April 2019
(This article belongs to the Special Issue Hydrologic Modelling for Water Resources and River Basin Management)
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Abstract

Accurate prediction of daily streamflow plays an essential role in various applications of water resources engineering, such as flood mitigation and urban and agricultural planning. This study investigated a hybrid ensemble decomposition technique based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) with gene expression programming (GEP) and random forest regression (RFR) algorithms for daily streamflow simulation across three mountainous stations, Siira, Bilghan, and Gachsar, in Karaj, Iran. To determine the appropriate corresponding input variables with optimal lag time the partial auto-correlation function (PACF) and auto-correlation function (ACF) were used for streamflow prediction purpose. Calibration and validation datasets were separately decomposed by EEMD that eventually improved standalone predictive models. Further, the component of highest pass (IMF1) was decomposed by the VMD approach to breakdown the distinctive characteristic of the variables. Results suggested that the EEMD-VMD algorithm significantly enhanced model calibration. Moreover, the EEMD-VMD-RFR algorithm as a hybrid ensemble model outperformed better than other techniques (EEMD-VMD-GEP, RFR and GEP) for daily streamflow prediction of the selected gauging stations. Overall, the proposed methodology indicated the superiority of hybrid ensemble models compare to standalone in predicting streamflow time series particularly in case of high fluctuations and different patterns in datasets. View Full-Text
Keywords: streamflow; ensemble empirical mode decomposition (EEMD); variational mode decomposition (VMD); artificial intelligence (AI) approach; mountainous watershed streamflow; ensemble empirical mode decomposition (EEMD); variational mode decomposition (VMD); artificial intelligence (AI) approach; mountainous watershed
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Rezaie-Balf, M.; Fani Nowbandegani, S.; Samadi, S.Z.; Fallah, H.; Alaghmand, S. An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction. Water 2019, 11, 709.

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