Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches
Sediment transportation and accurate estimation of its rate is a significant issue for river engineers and researchers. In this study, the capability of kernel based approaches including Kernel Extreme Learning Machine (KELM) and Gaussian Process Regression (GPR) was assessed for predicting the rive...
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2021
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oai:doaj.org-article:2ba8041bcf2c43f596107aabd15310a22021-11-23T18:55:39ZSuspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches1606-97491607-079810.2166/ws.2021.094https://doaj.org/article/2ba8041bcf2c43f596107aabd15310a22021-11-01T00:00:00Zhttp://ws.iwaponline.com/content/21/7/3370https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Sediment transportation and accurate estimation of its rate is a significant issue for river engineers and researchers. In this study, the capability of kernel based approaches including Kernel Extreme Learning Machine (KELM) and Gaussian Process Regression (GPR) was assessed for predicting the river daily Suspended Sediment Discharge (SSD). For this aim, the Mississippi River, with three consecutive hydrometric stations, was selected as the case study. Based on the sediment and flow characteristics during the period of 2005–2008, several models were developed and tested under two scenarios (i.e. modeling based on each station's own data or the previous stations' data). Two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used for enhancing the SSD modeling capability. Also, data post-proceeding was done using Simple Linear Averaging (SLAM) and Nonlinear Kernel Extreme Learning Machine Ensemble (NKELME) methods. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the models' capability up to 35%. It was found that SSD modeling based on the station's own data led to better results; however, using the integrated approaches, the previous station's data could be applied successfully for the SSD modeling when a station's own data were not available. HIGHLIGHTS Merge the advantages of the pre-post-processing and kernel based techniques for suspended sediment discharge prediction.; Two states of modeling based on a station's own data or the previous stations’ data were investigated.; Integrated hybrid techniques outperformed the single meta-model approaches.;Roghayeh GhasempourKiyoumars RoushangarParveen SihagIWA Publishingarticleeemdgprpre-processingsuccessive stationssuspended sediment loadWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 7, Pp 3370-3386 (2021) |
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eemd gpr pre-processing successive stations suspended sediment load Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 |
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eemd gpr pre-processing successive stations suspended sediment load Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Roghayeh Ghasempour Kiyoumars Roushangar Parveen Sihag Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches |
description |
Sediment transportation and accurate estimation of its rate is a significant issue for river engineers and researchers. In this study, the capability of kernel based approaches including Kernel Extreme Learning Machine (KELM) and Gaussian Process Regression (GPR) was assessed for predicting the river daily Suspended Sediment Discharge (SSD). For this aim, the Mississippi River, with three consecutive hydrometric stations, was selected as the case study. Based on the sediment and flow characteristics during the period of 2005–2008, several models were developed and tested under two scenarios (i.e. modeling based on each station's own data or the previous stations' data). Two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used for enhancing the SSD modeling capability. Also, data post-proceeding was done using Simple Linear Averaging (SLAM) and Nonlinear Kernel Extreme Learning Machine Ensemble (NKELME) methods. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the models' capability up to 35%. It was found that SSD modeling based on the station's own data led to better results; however, using the integrated approaches, the previous station's data could be applied successfully for the SSD modeling when a station's own data were not available. HIGHLIGHTS
Merge the advantages of the pre-post-processing and kernel based techniques for suspended sediment discharge prediction.;
Two states of modeling based on a station's own data or the previous stations’ data were investigated.;
Integrated hybrid techniques outperformed the single meta-model approaches.; |
format |
article |
author |
Roghayeh Ghasempour Kiyoumars Roushangar Parveen Sihag |
author_facet |
Roghayeh Ghasempour Kiyoumars Roushangar Parveen Sihag |
author_sort |
Roghayeh Ghasempour |
title |
Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches |
title_short |
Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches |
title_full |
Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches |
title_fullStr |
Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches |
title_full_unstemmed |
Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches |
title_sort |
suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches |
publisher |
IWA Publishing |
publishDate |
2021 |
url |
https://doaj.org/article/2ba8041bcf2c43f596107aabd15310a2 |
work_keys_str_mv |
AT roghayehghasempour suspendedsedimentloadpredictioninconsecutivestationsofriverbasedonensembleprepostprocessingkernelbasedapproaches AT kiyoumarsroushangar suspendedsedimentloadpredictioninconsecutivestationsofriverbasedonensembleprepostprocessingkernelbasedapproaches AT parveensihag suspendedsedimentloadpredictioninconsecutivestationsofriverbasedonensembleprepostprocessingkernelbasedapproaches |
_version_ |
1718416141789954048 |