The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river

Sediment transport is one of the most important issues in river engineering. In this study, the capability of the Kernel Extreme Learning Machine (KELM) approach for predicting the river daily Suspended Sediment Concentration (SSC) and Discharge (SSD) was assessed. Three successive hydrometric stati...

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Autores principales:	Kiyoumars Roushangar, Nasrin Aghajani, Roghayeh Ghasempour, Farhad Alizadeh
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	eemd kelm pre-processing suspended load suspended sediment discharge wt Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/19ca03f6a07e478795ae84e02f88bac5
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spelling	oai:doaj.org-article:19ca03f6a07e478795ae84e02f88bac52021-11-05T17:47:00ZThe potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river1464-71411465-173410.2166/hydro.2021.146https://doaj.org/article/19ca03f6a07e478795ae84e02f88bac52021-05-01T00:00:00Zhttp://jh.iwaponline.com/content/23/3/655https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734Sediment transport is one of the most important issues in river engineering. In this study, the capability of the Kernel Extreme Learning Machine (KELM) approach for predicting the river daily Suspended Sediment Concentration (SSC) and Discharge (SSD) was assessed. Three successive hydrometric stations of Mississippi river were considered and based on the sediment and flow characteristics during the period of 2005–2008. Several models were developed and tested for SSC and SSD modeling. For improving the applied model efficiency, two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used. Also, two states of modeling based on stations' own data (state 1) and previous stations' data (state 2) were considered. The single and integrated KELM model results comparison indicated that the integrated WT and EEMD-KELM models resulted in more accurate outcomes. Results showed that data processing with WT was more effective than EEMD in increasing the models' efficiency. Data processing enhanced the models' capability by up to 15%. The results showed that the state 1 modeling led to better results, however, using the integrated KELM approaches the previous stations data could be applied successfully for SSC and SSD modeling when the stations' own data were not available. HIGHLIGHT The suspended sediment concentration (SSC) and suspended sediment discharge (SSD) were predicted via artificial intelligence approach in successive hydrometric stations. The data pre-processing impacts on models' efficiency improvement was assessed. The sensitivity analysis showed the most effective subseries was obtained from pre-processing models.;Kiyoumars RoushangarNasrin AghajaniRoghayeh GhasempourFarhad AlizadehIWA Publishingarticleeemdkelmpre-processingsuspended loadsuspended sediment dischargewtInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 3, Pp 655-670 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	eemd kelm pre-processing suspended load suspended sediment discharge wt Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	eemd kelm pre-processing suspended load suspended sediment discharge wt Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Kiyoumars Roushangar Nasrin Aghajani Roghayeh Ghasempour Farhad Alizadeh The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river
description	Sediment transport is one of the most important issues in river engineering. In this study, the capability of the Kernel Extreme Learning Machine (KELM) approach for predicting the river daily Suspended Sediment Concentration (SSC) and Discharge (SSD) was assessed. Three successive hydrometric stations of Mississippi river were considered and based on the sediment and flow characteristics during the period of 2005–2008. Several models were developed and tested for SSC and SSD modeling. For improving the applied model efficiency, two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used. Also, two states of modeling based on stations' own data (state 1) and previous stations' data (state 2) were considered. The single and integrated KELM model results comparison indicated that the integrated WT and EEMD-KELM models resulted in more accurate outcomes. Results showed that data processing with WT was more effective than EEMD in increasing the models' efficiency. Data processing enhanced the models' capability by up to 15%. The results showed that the state 1 modeling led to better results, however, using the integrated KELM approaches the previous stations data could be applied successfully for SSC and SSD modeling when the stations' own data were not available. HIGHLIGHT The suspended sediment concentration (SSC) and suspended sediment discharge (SSD) were predicted via artificial intelligence approach in successive hydrometric stations. The data pre-processing impacts on models' efficiency improvement was assessed. The sensitivity analysis showed the most effective subseries was obtained from pre-processing models.;
format	article
author	Kiyoumars Roushangar Nasrin Aghajani Roghayeh Ghasempour Farhad Alizadeh
author_facet	Kiyoumars Roushangar Nasrin Aghajani Roghayeh Ghasempour Farhad Alizadeh
author_sort	Kiyoumars Roushangar
title	The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river
title_short	The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river
title_full	The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river
title_fullStr	The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river
title_full_unstemmed	The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river
title_sort	potential of ensemble wt-eemd-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/19ca03f6a07e478795ae84e02f88bac5
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The potential of ensemble WT-EEMD-kernel extreme learning machine techniques for prediction suspended sediment concentration in successive points of a river

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